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Exploring Neural Models for Query-Focused Summarization [article]

Jesse Vig, Alexander R. Fabbri, Wojciech Kryściński, Chien-Sheng Wu, Wenhao Liu
2022 arXiv   pre-print
Within those categories, we investigate existing models and explore strategies for transfer learning.  ...  Query-focused summarization (QFS) aims to produce summaries that answer particular questions of interest, enabling greater user control and personalization.  ...  A Appendix Locator Model Parameters For MARGE experiments, we apply the original fine-tuned BERTbase checkpoint from Xu and Lapata (2021a), while for DPR, we fine-tune a BERT-base model for both query  ... 
arXiv:2112.07637v3 fatcat:vmgt2s4qh5d77o4fngzarg7bdi

Exploring Neural Models for Query-Focused Summarization

Jesse Vig, Alexander Fabbri, Wojciech Kryscinski, Chien-Sheng Wu, Wenhao Liu
2022 Findings of the Association for Computational Linguistics: NAACL 2022   unpublished
Within those categories, we investigate existing models and explore strategies for transfer learning.  ...  Query-focused summarization (QFS) aims to produce summaries that answer particular questions of interest, enabling greater user control and personalization.  ...  A Appendix Locator Model Parameters For MARGE experiments, we apply the original fine-tuned BERTbase checkpoint from Xu and Lapata (2021a), while for DPR, we fine-tune a BERT-base model for both query  ... 
doi:10.18653/v1/2022.findings-naacl.109 fatcat:xgkcto36n5a5fnagfcfh4n6omi

AttSum: Joint Learning of Focusing and Summarization with Neural Attention [article]

Ziqiang Cao, Wenjie Li, Sujian Li, Furu Wei, Yanran Li
2016 arXiv   pre-print
Query relevance ranking and sentence saliency ranking are the two main tasks in extractive query-focused summarization.  ...  Extensive experiments are conducted on DUC query-focused summarization benchmark datasets. Without using any hand-crafted features, AttSum achieves competitive performance.  ...  Thus, a joint neural network model should be a nice solution to extractive query-focused summarization.  ... 
arXiv:1604.00125v2 fatcat:23i4lnfxljgxfdlow3k3omrdhi

Transforming Wikipedia into Augmented Data for Query-Focused Summarization [article]

Haichao Zhu, Li Dong, Furu Wei, Bing Qin, Ting Liu
2022 arXiv   pre-print
The limited size of existing query-focused summarization datasets renders training data-driven summarization models challenging.  ...  We also develop a BERT-based query-focused summarization model (Q-BERT) to extract sentences from the documents as summaries.  ...  The small data size is the main obstacle to develop neural models for query-focused summarization. Wikipedia Wikipedia provides rich resources for exploring various natural language processing tasks.  ... 
arXiv:1911.03324v2 fatcat:ofoyvu2tvzazjoomvo2u5ot7ju

Pre-training Methods in Information Retrieval [article]

Yixing Fan, Xiaohui Xie, Yinqiong Cai, Jia Chen, Xinyu Ma, Xiangsheng Li, Ruqing Zhang, Jiafeng Guo
2022 arXiv   pre-print
In addition, we also introduce PTMs specifically designed for IR, and summarize available datasets as well as benchmark leaderboards.  ...  ., neural information retrieval), especially the paradigm of pre-training methods (PTMs).  ...  ., 2019) , which developed a BERT-based query-focused summarization model.  ... 
arXiv:2111.13853v3 fatcat:pilemnpphrgv5ksaktvctqdi4y

Neural information retrieval: introduction to the special issue

Nick Craswell, W. Bruce Croft, Maarten de Rijke, Jiafeng Guo, Bhaskar Mitra
2017 Information retrieval (Boston)  
Acknowledgements We thank Charles Clarke, co-editor of the journal, for his guidance during the editing of this special issue.  ...  Wang et al. (2017) explore neural models for context-aware music recommendation.  ...  The body of this work then focuses on providing a broad taxonomy of neural models for different IR tasks and scenarios.  ... 
doi:10.1007/s10791-017-9323-9 fatcat:xzl5wtc67bgwhiru2prdw4k6oe

Domain Adaptation with Pre-trained Transformers for Query-Focused Abstractive Text Summarization

Md Tahmid Rahman Laskar, Enamul Hoque, Jimmy Xiangji Huang
2022 Computational Linguistics  
The Query-Focused Text Summarization (QFTS) task aims at building systems that generate the summary of the text document(s) based on the given query.  ...  A key challenge in addressing this task is the lack of large labeled data for training the summarization model.  ...  We show the results of our experiments in Table C1 to find that in terms of both Recall and F1, the original model that was fine-tuned on the MS-MARCO dataset for the final summary selection performs  ... 
doi:10.1162/coli_a_00434 fatcat:q7q7pqnyf5flvao7uiuqiptira

Neural Ranking Models for Document Retrieval [article]

Mohamed Trabelsi, Zhiyu Chen, Brian D. Davison, Jeff Heflin
2021 arXiv   pre-print
A variety of deep learning models have been proposed, and each model presents a set of neural network components to extract features that are used for ranking.  ...  These models are trained end-to-end to extract features from the raw data for ranking tasks, so that they overcome the limitations of hand-crafted features.  ...  Abcnn: Attention-based convolutional neural network for modeling sentence pairs. Transactions of the Association for Computational Linguistics, 4, 259-272.  ... 
arXiv:2102.11903v1 fatcat:zc2otf456rc2hj6b6wpcaaslsa

On Extractive Summarization for Profile-centric Neural Expert Search in Academia

Rennan C. Lima, Rodrygo L. T. Santos
2022 Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval  
A key challenge for ranking experts in response to a query is how to infer their expertise from the publications they coauthored.  ...  CCS CONCEPTS • Information systems → Language models.  ...  Recent document-centric approaches leverage contextual neural language models [14] for producing an improved query-biased sample of publications [9] .  ... 
doi:10.1145/3477495.3531713 fatcat:feburplfjnfktgub35fgevdble

Query-Focused Video Summarization: Dataset, Evaluation, and A Memory Network Based Approach [article]

Aidean Sharghi, Jacob S. Laurel, Boqing Gong
2017 arXiv   pre-print
To tackle the first problem, we explore the recently proposed query-focused video summarization which introduces user preferences in the form of text queries about the video into the summarization process  ...  To address the second challenge, we contend that a good evaluation metric for video summarization should focus on the semantic information that humans can perceive rather than the visual features or temporal  ...  We thank Fei Sha, the anonymous reviewers and area chairs, especially R2, for their insightful suggestions.  ... 
arXiv:1707.04960v1 fatcat:6uxguznftzh5zdw3hm4mqgsuzy

ABNIRML: Analyzing the Behavior of Neural IR Models [article]

Sean MacAvaney, Sergey Feldman, Nazli Goharian, Doug Downey, Arman Cohan
2020 arXiv   pre-print
We present a new comprehensive framework for Analyzing the Behavior of Neural IR ModeLs (ABNIRML), which includes new types of diagnostic tests that allow us to probe several characteristics---such as  ...  We find evidence that recent neural ranking models have fundamentally different characteristics from prior ranking models.  ...  Acknowledgements We thank Mark Neumann for his helpful feedback on this work.  ... 
arXiv:2011.00696v1 fatcat:x5tzhhnxhjg4nakk7hibvlvwwu

Multi-Task Learning for Document Ranking and Query Suggestion

Wasi Uddin Ahmad, Kai-Wei Chang, Hongning Wang
2018 International Conference on Learning Representations  
We propose a multi-task learning framework to jointly learn document ranking and query suggestion for web search. It consists of two major components, a document ranker and a query recommender.  ...  Query recommender tracks users' query reformulation sequence considering all previous in-session queries using a sequence to sequence approach.  ...  Existing neural ranking models fall into two categories: representation focused (Huang et al., 2013; and interaction focused (Guo et al., 2016b) .  ... 
dblp:conf/iclr/AhmadCW18 fatcat:iqcucxmg65gt3a4xgmhf5majny

A survey on sentimental cluster based opinion summarization in question answering community

Ankur Goswami
2019 International Journal of Advanced Technology and Engineering Exploration  
Paper Model used Features Outcome Liu et al. [14], 2015 Two-stage approach Query likelihood language model that retrieve the questions and recurrent neural network (RNN) encoder-decoder, a sequence-to-sequence  ...  It is improved for machine translation to summarization and also using the convolutional neural networks (CNN) and long short-term memory (LSTM) to develop the performance of text summarization [3−6]  ... 
doi:10.19101/ijatee.2019.650016 fatcat:zxuraar6azfctfhhzrnjqoro3a

Leveraging Contextual Sentence Relations for Extractive Summarization Using a Neural Attention Model

Pengjie Ren, Zhumin Chen, Zhaochun Ren, Furu Wei, Jun Ma, Maarten de Rijke
2017 Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR '17  
We propose a neural network model, Contextual Relation-based Summarization (CRSum), to take advantage of contextual relations among sentences so as to improve the performance of sentence regression.  ...  As a framework for extractive summarization, sentence regression has achieved state-of-the-art performance in several widely-used practical systems. e most challenging task within the sentence regression  ...  2007 datasets are for query-focused multi-document summarization. 2 we evaluate the e ectiveness of contextual features on the DUC 2005, 2006 and 2007 query-focused multi-document summarization datasets  ... 
doi:10.1145/3077136.3080792 dblp:conf/sigir/RenCRWMR17 fatcat:s6tsgpkvz5c7lczqcwbwmdrvxq

NeuralCubes: Deep Representations for Visual Data Exploration [article]

Zhe Wang, Dylan Cashman, Mingwei Li, Jixian Li, Matthew Berger, Joshua A. Levine, Remco Chang, Carlos Scheidegger
2019 arXiv   pre-print
To tackle this problem, we present NeuralCubes: neural networks that predict results for aggregate queries, similar to data cubes.  ...  NeuralCubes models are small enough to be sent to the client side (e.g. the web browser for a web-based application) for evaluation, enabling data exploration of large datasets without database/network  ...  These techniques are solely focused on prediction, and our method is similar, in that we are focused on training deep networks for the purposes of query prediction.  ... 
arXiv:1808.08983v3 fatcat:hhd7angzebbl5dapa5y6atoycq
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