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Recent Trends in Deep Learning Based Abstractive Text Summarization

2019 International journal of recent technology and engineering  
It is open problem in Natural Language Processing (NLP) and a difficult work for humans to understand and generate an abstract manually while it have need of a accurate analysis of the document.  ...  From these two methods, abstractive text summarization is laborious task to realize as it needs correct understanding and sentence amalgamation.  ...  Khan et al., [19] introduced a semantic graph approach clubbed with an efficient sentence ranking algorithm for abstractive summarization of multi-documents.  ... 
doi:10.35940/ijrte.c4996.098319 fatcat:xvzhfor7cfdujl23cewmlbcdmy

Asking Complex Questions with Multi-hop Answer-focused Reasoning [article]

Xiyao Ma, Qile Zhu, Yanlin Zhou, Xiaolin Li, Dapeng Wu
2020 arXiv   pre-print
To solve the problem, we propose multi-hop answer-focused reasoning on the grounded answer-centric entity graph to include different granularity levels of semantic information including the word-level  ...  relations given a collection of documents and the corresponding answer 1.  ...  the answer node offers the answer-aware representation for the graph reasoning, and models a global representation across documents.  ... 
arXiv:2009.07402v1 fatcat:woidlj2rf5anrcwxgipkri5nmm

DocAMR: Multi-Sentence AMR Representation and Evaluation [article]

Tahira Naseem, Austin Blodgett, Sadhana Kumaravel, Tim O'Gorman, Young-Suk Lee, Jeffrey Flanigan, Ramón Fernandez Astudillo, Radu Florian, Salim Roukos, Nathan Schneider
2021 arXiv   pre-print
Next, we describe improvements to the Smatch metric to make it tractable for comparing document-level graphs, and use it to re-evaluate the best published document-level AMR parser.  ...  Taking advantage of a super-sentential level of coreference annotation from previous work, we introduce a simple algorithm for deriving a unified graph representation, avoiding the pitfalls of information  ...  In We have presented D OC AMR, a graph represen- Proceedings of the 59th Annual Meeting of the tation for document-level AMR graphs based on Association for Computational Linguistics  ... 
arXiv:2112.08513v1 fatcat:bbl7s4n7bbdshib67solbxlady

Top approaches to abstractive text summarization: A survey

2020 International Journal of Emerging Trends in Engineering Research  
Abstractive text summarization system understands, interpret the original text and presents the text in new form therefore abstractive summarization require more engineering and linguistic efforts.  ...  In text mining, summarization is a task where the gist of the source text is generated by the system. Extractive and abstractive are the two variants of text summarization based on context.  ...  Khan, A., Salim, N., & Kumar, Y. J., "A framework for multi-document abstractive summarization based on semantic role labeling", Applied Soft Computing, vol. 30, pp. 737-747, 2015. 47. Ren, F.  ... 
doi:10.30534/ijeter/2020/1058102020 fatcat:xxammqkunjdwve3omipqcor6qq

HETFORMER: Heterogeneous Transformer with Sparse Attention for Long-Text Extractive Summarization [article]

Ye Liu, Jian-Guo Zhang, Yao Wan, Congying Xia, Lifang He, Philip S. Yu
2021 arXiv   pre-print
To mitigate these issues, this paper proposes HETFORMER, a Transformer-based pre-trained model with multi-granularity sparse attentions for long-text extractive summarization.  ...  However, these methods suffer from cumbersome procedures and inefficient computations for long-text documents.  ...  Acknowledgements We would like to thank all the reviewers for their helpful comments. This work is supported by NSF under grants III-1763325, III-1909323, III-2106758, and SaTC-1930941.  ... 
arXiv:2110.06388v2 fatcat:jn3qpnqq7ffvrgjb7nbtu6zusq

Comparative Study of Text Summarization Methods

Nikita Munot, Sharvari S. Govilkar
2014 International Journal of Computer Applications  
Text summarization is a process of reducing the size of original document and producing a summary by retaining important information of original document.  ...  The paper also presents taxonomy of summarization systems and statistical and linguistic approaches for summarization.  ...  A method that creates a semantic graph of a document, based on logical form triples subject-predicate-object (SPO), and learns a relevant sub-graph that could be used for creating summaries.  ... 
doi:10.5120/17870-8810 fatcat:pkxztoat7rhszbh7q2rkv26m7i

Graph Neural Networks for Natural Language Processing: A Survey [article]

Lingfei Wu, Yu Chen, Kai Shen, Xiaojie Guo, Hanning Gao, Shucheng Li, Jian Pei, Bo Long
2021 arXiv   pre-print
We propose a new taxonomy of GNNs for NLP, whichsystematically organizes existing research of GNNs for NLP along three axes: graph construction,graph representation learning, and graph based encoder-decoder  ...  As a result, thereis a surge of interests in developing new deep learning techniques on graphs for a large numberof NLP tasks.  ...  First, we provide the meta-level (i.e., schema-level) description of a heterogeneous graph for better understanding.  ... 
arXiv:2106.06090v1 fatcat:zvkhinpcvzbmje4kjpwjs355qu

Mining Conceptual Relations from Textual Web Content Using Leximancer

Ms.C Thavamani, Dr.A Rengarajan
2014 IOSR Journal of Computer Engineering  
At the text representation level, we introduce a sentence based conceptual ontological representation that builds concept-based representations for the whole document.  ...  This work presents a new prototype for concept mining by extracting the concept-based information from a raw text using leximancer.  ...  Second, it presents different levels of depth for understanding the meaning of a sentence.  ... 
doi:10.9790/0661-16552427 fatcat:aqor36zu3bdavhv3emgvr232qi

Better Modeling the Programming World with Code Concept Graphs-augmented Multi-modal Learning [article]

Martin Weyssow, Houari Sahraoui, Bang Liu
2022 arXiv   pre-print
In particular, we propose to enhance an existing pretrained language model of code by joint-learning it with a graph neural network based on our concept graphs.  ...  In this paper, we investigate one of the underlying idea of our vision whose objective based on concept graphs of identifiers aims at leveraging high-level relationships between domain concepts manipulated  ...  By joint-learning a LM based on code tokens and a GNN based on concept graphs, we expect both models to learn better vector representations that incorporate more semantics by covering different level of  ... 
arXiv:2201.03346v1 fatcat:67ilomujwrbmjiaghg3ljanlbu

Multi-document Summarization via Deep Learning Techniques: A Survey [article]

Congbo Ma, Wei Emma Zhang, Mingyu Guo, Hu Wang, Quan Z. Sheng
2021 arXiv   pre-print
Multi-document summarization (MDS) is an effective tool for information aggregation that generates an informative and concise summary from a cluster of topic-related documents.  ...  Our survey, the first of its kind, systematically overviews the recent deep learning based MDS models.  ...  For each input document, the condense model leverages a Bi-LSTM auto-encoder to learn document-level and word-level representations.  ... 
arXiv:2011.04843v3 fatcat:zfi52xxef5g2tjkaw6hgjpwa5i

Automatic Authorship Detection Using Textual Patterns Extracted from Integrated Syntactic Graphs

Helena Gómez-Adorno, Grigori Sidorov, David Pinto, Darnes Vilariño, Alexander Gelbukh
2016 Sensors  
This graph-based representation allows integrating different levels of language description into a single structure.  ...  We extract textual patterns based on features obtained from shortest path walks over integrated syntactic graphs and apply them to determine the authors of documents.  ...  While traversing the paths, we count the occurrences of all multi-level linguistic features considered in the text representation.  ... 
doi:10.3390/s16091374 pmid:27589740 pmcid:PMC5038652 fatcat:ieymx7ngzzacbbodluf7bt6poe

Semantic Representation for Dialogue Modeling [article]

Xuefeng Bai, Yulong Chen, Linfeng Song, Yue Zhang
2021 arXiv   pre-print
We develop an algorithm to construct dialogue-level AMR graphs from sentence-level AMRs and explore two ways to incorporate AMRs into dialogue systems.  ...  To our knowledge, we are the first to leverage a formal semantic representation into neural dialogue modeling.  ...  A discrim- line. Association for Computational Linguistics. inative graph-based parser for the Abstract Meaning Representation.  ... 
arXiv:2105.10188v2 fatcat:unzjukziljbc7lqs4w722gczh4

A Survey of Text Summarization Extractive Techniques

Vishal Gupta, Gurpreet Singh Lehal
2010 Journal of Emerging Technologies in Web Intelligence  
The importance of sentences is decided based on statistical and linguistic features of sentences.  ...  It is very difficult for human beings to manually summarize large documents of text. Text Summarization methods can be classified into extractive and abstractive summarization.  ...  Figure2 shows an example graph for a document.  ... 
doi:10.4304/jetwi.2.3.258-268 fatcat:anms2x4aczftdnedejeo2tznwm

Literature Review on Extractive Text Summarization Approaches

Saiyed Saziyabegum, Priti S.
2016 International Journal of Computer Applications  
Extractive approach uses linguistic and statistical approach for selection of sentences for summary.  ...  Abstractive methods are requires deep understanding of text. After understanding, it represents text into new simple notions in shorter form.  ...  Graph theoretic approach The graph based approach is used for extracting the most relevant sentences from the original document to form a summary.  ... 
doi:10.5120/ijca2016912574 fatcat:zwxchjs5wve57jrmgd2nkl4jhm

Interactive Machine Comprehension with Dynamic Knowledge Graphs [article]

Xingdi Yuan
2021 arXiv   pre-print
Extensive experiments on iSQuAD suggest that graph representations can result in significant performance improvements for RL agents.  ...  We explore four different categories of graphs that can capture text information at various levels.  ...  We also thank the anonymous EMNLP reviewers and area chairs for their helpful feedback and suggestions.  ... 
arXiv:2109.00077v1 fatcat:gozbgg6ctzgbheghqdukvhlfsa
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