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Learning Sentence Embeddings for Coherence Modelling and Beyond [article]

Tanner Bohn, Yining Hu, Jinhang Zhang, Charles X. Ling
<span title="2019-08-07">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
To produce these sentence embeddings, we train a recurrent neural network to take individual sentences and predict their location in a document in the form of a distribution over locations.  ...  Additionally, we demonstrate that these embeddings can provide insights useful to writers for improving writing quality and informing document structuring, and assisting readers in summarizing and locating  ...  As a neural model, we use a bidirectional recurrent neural network, and train it to take sentences and predict a discrete distribution over possible locations in the source text.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1804.08053v2">arXiv:1804.08053v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/s7zltj7offcwpozpvaqzpuqera">fatcat:s7zltj7offcwpozpvaqzpuqera</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200929223040/https://arxiv.org/pdf/1804.08053v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/2a/7b/2a7b672a8fbf071b468d11268b981af6038ef0a0.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1804.08053v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Multi-Document Abstractive Summarization using Recursive Neural Network

<span title="2020-05-10">2020</span> <i title="Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/cj3bm7tgcffurfop7xzswxuks4" style="color: black;">VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE</a> </i> &nbsp;
With the application of neural networks for text generation, interest for research in abstractive text summarization has increased significantly.  ...  The use of neural networks allows generation of summaries for long text sentences as well. The work implements semantic based filtering using a similarity matrix while keeping all stop-words.  ...  One type of neural network is the recursive neural network which makes use of the same weights recursively on a structured input to predict the output.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.35940/ijitee.g5274.059720">doi:10.35940/ijitee.g5274.059720</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/h6jwnhq74fgizeyv2x54dokxry">fatcat:h6jwnhq74fgizeyv2x54dokxry</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220303112704/https://www.ijitee.org/wp-content/uploads/papers/v9i7/G5274059720.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/75/c1/75c1dab32334eb4ada4d2d31eca29ce71f434d58.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.35940/ijitee.g5274.059720"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Finding Summary of Text Using Neural Networks and Rhetorical Structure Theory

<span title="2016-05-05">2016</span> <i title="International Journal of Science and Research"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/odv46yibm5gf7evzoopmrrqr3q" style="color: black;">International Journal of Science and Research (IJSR)</a> </i> &nbsp;
A new technique for summarization is presented here for summarizing articles known as finding summary of text using neural network and rhetorical structure theory.  ...  A neural network is trained to learn the relevant characteristics of sentences by using back propagation technique to train the neural network which will be used in the summary of the article.  ...  , which finds sentence location among all sentences from paragraph and decides rank for sentences as per their position.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.21275/v5i5.nov163987">doi:10.21275/v5i5.nov163987</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/uoap33bi5bhcppv4o2ostveday">fatcat:uoap33bi5bhcppv4o2ostveday</a> </span>
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The State of the Art on Knowledge Graph Construction from Text: Named Entity Recognition and Relation Extraction Perspectives

Jennifer D'Souza, Nandana Mihindukulasooriya
<span title="2022-05-05">2022</span> <i title="Zenodo"> Zenodo </i> &nbsp;
This presentation covers the state-of-the-art benchmark dataset resources as well as the best neural models for the respective task perspectives.  ...  This sequence is passed through an RNN, predicting labels for each character. ○ Character labels transformed into word labels via post processing• Kim et al. (2016) character-level neural network model  ...  ○ used highway networks over convolution neural networks (CNN) on character sequences of words and then used another layer of LSTM + softmax for the final predictions Reference Kim, Yoon, Yacine Jernite  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5281/zenodo.6521883">doi:10.5281/zenodo.6521883</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/swra4e7rf5dljhjojl7nc2cq4i">fatcat:swra4e7rf5dljhjojl7nc2cq4i</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220505150912/https://zenodo.org/record/6521883/files/Slides%20for%20KGC%202022.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/62/14/621472daef94c4e13097b4a07d5d83100f4cff29.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5281/zenodo.6521883"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> zenodo.org </button> </a>

Identifying Where to Focus in Reading Comprehension for Neural Question Generation

Xinya Du, Claire Cardie
<span title="">2017</span> <i title="Association for Computational Linguistics"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/u3ideoxy4fghvbsstiknuweth4" style="color: black;">Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing</a> </i> &nbsp;
We propose a hierarchical neural sentence-level sequence tagging model for this task, which existing approaches to question generation have ignored.  ...  A first step in the task of automatically generating questions for testing reading comprehension is to identify questionworthy sentences, i.e. sentences in a text passage that humans find it worthwhile  ...  Acknowledgments We thank the reviewers for helpful comments and Victoria Litvinova for proofreading.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.18653/v1/d17-1219">doi:10.18653/v1/d17-1219</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/emnlp/DuC17.html">dblp:conf/emnlp/DuC17</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7emyaygnhrcy7pu3h2dagko2me">fatcat:7emyaygnhrcy7pu3h2dagko2me</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200309090101/https://www.aclweb.org/anthology/D17-1219.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/86/80/8680ea1fc64077189e5ea6265e974d042deb940b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.18653/v1/d17-1219"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

genCNN: A Convolutional Architecture for Word Sequence Prediction [article]

Mingxuan Wang, Zhengdong Lu, Hang Li, Wenbin Jiang, Qun Liu
<span title="2015-04-24">2015</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We propose a novel convolutional architecture, named genCNN, for word sequence prediction.  ...  Instead, we use a convolutional neural network to predict the next word with the history of words of variable length.  ...  Our extensive experiments on sentence generation, perplexity, and n-best re-ranking for machine translation show that our model can significantly improve upon state-of-the-arts.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1503.05034v2">arXiv:1503.05034v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xhnmdoxoszfc7jl7lwl5wowqaa">fatcat:xhnmdoxoszfc7jl7lwl5wowqaa</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200905025504/https://arxiv.org/pdf/1503.05034v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/60/e3/60e3cb87cc5d971dc3e416ac10143b023a24a0fb.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1503.05034v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

genCNN: A Convolutional Architecture for Word Sequence Prediction

Mingxuan Wang, Zhengdong Lu, Hang Li, Wenbin Jiang, Qun Liu
<span title="">2015</span> <i title="Association for Computational Linguistics"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/5n6volmnonf5tn6xputi5f2t3e" style="color: black;">Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)</a> </i> &nbsp;
We propose a convolutional neural network, named genCNN, for word sequence prediction.  ...  Instead, we use a convolutional neural network to predict the next word with the history of words of variable length.  ...  Our extensive experiments on sentence generation, perplexity, and n-best re-ranking for machine translation show that our model can significantly improve upon state-of-the-arts.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3115/v1/p15-1151">doi:10.3115/v1/p15-1151</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/acl/WangLLJL15.html">dblp:conf/acl/WangLLJL15</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/fvxljvdlofetvecchgkxwx72mu">fatcat:fvxljvdlofetvecchgkxwx72mu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200505073418/https://www.aclweb.org/anthology/P15-1151.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/1b/ce/1bce7021762989be033f3af42e329109e3c152a9.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3115/v1/p15-1151"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

EFFICIENT TEXT SUMMARIZER USING POINT TO GENERATOR TECHNIQUE

G. Lasya Sriranga, P. Likitha, B. Meghana, N. Jayanthi
<span title="2020-05-31">2020</span> <i title="IJEAST"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/namhphsg6rdvtofp27o3scimoy" style="color: black;">International Journal of Engineering Applied Sciences and Technology</a> </i> &nbsp;
Many people locate it tough to read the whole passage of records so that it will gather the essential key factors, so we use textual content summarization to reduce the burden of analyzing big passages  ...  Text summarization is been used in lots of application like business evaluation and marketplace overview.  ...  Sentence reduction for automatic text summarization: We gift a singular sentence discount device for mechanically disposing of extraneous terms from sentences which might be extracted from a file for summarization  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.33564/ijeast.2020.v05i01.086">doi:10.33564/ijeast.2020.v05i01.086</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5ftlnunrj5gc3doqp6bjklbcne">fatcat:5ftlnunrj5gc3doqp6bjklbcne</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210812003157/https://www.ijeast.com/papers/488-492,Tesma501,IJEAST.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/23/12/2312a846b11b2c871d61e99c20022f166ef6b7a5.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.33564/ijeast.2020.v05i01.086"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Dynamic Sliding Window for Meeting Summarization [article]

Zhengyuan Liu, Nancy F. Chen
<span title="2021-08-31">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Based on this observation, we propose a dynamic sliding window strategy for meeting summarization.  ...  Recently abstractive spoken language summarization raises emerging research interest, and neural sequence-to-sequence approaches have brought significant performance improvement.  ...  We then conducted a stride prediction assessment. For the Retrospective method described in Section 3.3, the predicted context boundaries are expected to be located closely to the ground-truth.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2108.13629v1">arXiv:2108.13629v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ls4y4rxtcrb2hgl53i3xcyy6je">fatcat:ls4y4rxtcrb2hgl53i3xcyy6je</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210904140549/https://arxiv.org/pdf/2108.13629v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/58/8e/588e56d62e18c58f4dfb83cec2af225cc48a780d.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2108.13629v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Leveraging Locality in Abstractive Text Summarization [article]

Yixin Liu, Ansong Ni, Linyong Nan, Budhaditya Deb, Chenguang Zhu, Ahmed H. Awadallah, Dragomir Radev
<span title="2022-05-25">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We empirically investigated three kinds of localities in text summarization at different levels, ranging from sentences to documents.  ...  Despite the successes of neural attention models for natural language generation tasks, the quadratic memory complexity of the self-attention module with respect to the input length hinders their applications  ...  Given a pair of an input document D and a reference summary S, the standard training algorithm of a neural abstractive summarization model g adopts the cross-entropy loss, which requires the model to predict  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2205.12476v1">arXiv:2205.12476v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/nyurpblaajc6lkohtn574owabq">fatcat:nyurpblaajc6lkohtn574owabq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220527075327/https://arxiv.org/pdf/2205.12476v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/b1/7d/b17dff882e7ad81a0a6733646e6b7ac89e749693.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2205.12476v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

BioCopy: A Plug-And-Play Span Copy Mechanism in Seq2Seq Models [article]

Yi Liu, Guoan Zhang, Puning Yu, Jianlin Su, Shengfeng Pan
<span title="2021-09-26">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Specifically, in the training stage, we construct a BIO tag for each token and train the original model with BIO tags jointly.  ...  Copy mechanisms explicitly obtain unchanged tokens from the source (input) sequence to generate the target (output) sequence under the neural seq2seq framework.  ...  ., 2019) utilize Transformers or graph neural network (Wang et al., 2020) for model encoder.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2109.12533v1">arXiv:2109.12533v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/apirzsoxibeipbldx7mdmudo5q">fatcat:apirzsoxibeipbldx7mdmudo5q</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210929151316/https://arxiv.org/pdf/2109.12533v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/8a/e9/8ae93e04053b95169889dfcefd4569f6a2994f3a.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2109.12533v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Neurophysiological, linguistic, and cognitive predictors of children's ability to perceive speech in noise

Elaine C. Thompson, Jennifer Krizman, Travis White-Schwoch, Trent Nicol, Ryne Estabrook, Nina Kraus
<span title="2019-08-06">2019</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/d6mayixwtng25h4i22gqinj7em" style="color: black;">Developmental Cognitive Neuroscience</a> </i> &nbsp;
While neural coding of the F0 supports perception in both co-located and spatially separated conditions, neural timing predicts perception of spatially separated listening exclusively.  ...  For instance, co-located speech and noise demands a large cognitive load and recruits working memory, while spatially separating speech and noise diminishes this load and draws on alternative skills.  ...  Acknowledgement We would like to thank the members of the Auditory Neuroscience Laboratory, past and present, for their contributions to this work, and the children and families who participated in this  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.dcn.2019.100672">doi:10.1016/j.dcn.2019.100672</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/31430627">pmid:31430627</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6886664/">pmcid:PMC6886664</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mx7aluvgxnce7dnlwmedyjdy3u">fatcat:mx7aluvgxnce7dnlwmedyjdy3u</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200511064252/http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC6886664&amp;blobtype=pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ec/24/ec24b798c7261ed7b9e05456f14485bd4218229c.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.dcn.2019.100672"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> elsevier.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6886664" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Exploring Encoder-Decoder Model for Distant Supervised Relation Extraction

Sen Su, Ningning Jia, Xiang Cheng, Shuguang Zhu, Ruiping Li
<span title="">2018</span> <i title="International Joint Conferences on Artificial Intelligence Organization"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/vfwwmrihanevtjbbkti2kc3nke" style="color: black;">Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence</a> </i> &nbsp;
Given an entity pair and its sentence bag as input, in the encoder component, we employ the convolutional neural network to extract the features of the sentences in the sentence bag and merge them into  ...  In this paper, we present an encoder-decoder model for distant supervised relation extraction.  ...  We acknowledge anonymous reviewers for their valuable comments.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.24963/ijcai.2018/610">doi:10.24963/ijcai.2018/610</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/ijcai/SuJ0ZL18.html">dblp:conf/ijcai/SuJ0ZL18</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jgshpwceavcwjkbv4mr4unw4s4">fatcat:jgshpwceavcwjkbv4mr4unw4s4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190223212404/http://pdfs.semanticscholar.org/4d76/ad0abd934b450f10cb05650f5aac6917401f.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/4d/76/4d76ad0abd934b450f10cb05650f5aac6917401f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.24963/ijcai.2018/610"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Neural Summarization by Extracting Sentences and Words [article]

Jianpeng Cheng, Mirella Lapata
<span title="2016-07-01">2016</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Traditional approaches to extractive summarization rely heavily on human-engineered features. In this work we propose a data-driven approach based on neural networks and continuous sentence features.  ...  We develop a general framework for single-document summarization composed of a hierarchical document encoder and an attention-based extractor.  ...  Acknowledgments We would like to thank three anonymous reviewers and members of the ILCC at the School of Informatics for their valuable feedback.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1603.07252v3">arXiv:1603.07252v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/svahhf5yz5dx5bdplgzg2qrq4u">fatcat:svahhf5yz5dx5bdplgzg2qrq4u</a> </span>
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Neural Summarization by Extracting Sentences and Words

Jianpeng Cheng, Mirella Lapata
<span title="">2016</span> <i title="Association for Computational Linguistics"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/5n6volmnonf5tn6xputi5f2t3e" style="color: black;">Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)</a> </i> &nbsp;
Traditional approaches to extractive summarization rely heavily on humanengineered features. In this work we propose a data-driven approach based on neural networks and continuous sentence features.  ...  We develop a general framework for single-document summarization composed of a hierarchical document encoder and an attention-based extractor.  ...  Acknowledgments We would like to thank three anonymous reviewers and members of the ILCC at the School of Informatics for their valuable feedback.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.18653/v1/p16-1046">doi:10.18653/v1/p16-1046</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/acl/0001L16.html">dblp:conf/acl/0001L16</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/usutfzsdhrakxn2d4xm2e36tou">fatcat:usutfzsdhrakxn2d4xm2e36tou</a> </span>
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