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A Bottom-Up Approach to Sentence Ordering for Multi-Document Summarization [chapter]

Danushka Bollegala, Naoaki Okazaki, Mitsuru Ishizuka
2012 Multi-source, Multilingual Information Extraction and Summarization  
We present a bottom-up approach to arranging sentences extracted for multi-document summarization.  ...  Ordering information is a difficult but important task for applications generating natural-language text.  ...  Results of semi-automatic evaluation Conclusion We present a bottom-up approach to arrange sentences extracted for multi-document summarization.  ... 
doi:10.1007/978-3-642-28569-1_12 dblp:series/tanlp/BollegalaOI13 fatcat:f2tlbuennzcvlpvamf4evroelm

A bottom-up approach to sentence ordering for multi-document summarization

Danushka Bollegala, Naoaki Okazaki, Mitsuru Ishizuka
2010 Information Processing & Management  
We present a bottom-up approach to arranging sentences extracted for multi-document summarization.  ...  Ordering information is a difficult but important task for applications generating natural-language text.  ...  Results of semi-automatic evaluation Conclusion We present a bottom-up approach to arrange sentences extracted for multi-document summarization.  ... 
doi:10.1016/j.ipm.2009.07.004 fatcat:exfztyxuqbgyhdcihs4q4fz6sm

A bottom-up approach to sentence ordering for multi-document summarization

Danushka Bollegala, Naoaki Okazaki, Mitsuru Ishizuka
2006 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the ACL - ACL '06  
We present a bottom-up approach to arranging sentences extracted for multi-document summarization.  ...  Ordering information is a difficult but important task for applications generating natural-language text.  ...  Results of semi-automatic evaluation Conclusion We present a bottom-up approach to arrange sentences extracted for multi-document summarization.  ... 
doi:10.3115/1220175.1220224 dblp:conf/acl/BollegalaOI06 fatcat:7piixnfj7bd6jd26zl7mvy3qtq

Knowledge Based Summarization and Document Generation using Bayesian Network

Shrikant Malviya, Uma Shanker Tiwary
2016 Procedia Computer Science  
In this paper an approach of Semantic Knowledge Extraction (SKE), from a set of research papers, is proposed to develop a system Summarized Research Article Generator (SRAG) which would generate a summarized  ...  Scores of all the entities are calculated in bottom to up manner, first score of words are calculated, based on words sentences are ranked and similarly all the higher levels of the knowledge tree would  ...  Multi Document Summarization would be useful for the users to quickly understand the central idea of document collection, and it has been shown that multi document summarization could also be used to improve  ... 
doi:10.1016/j.procs.2016.06.080 fatcat:zq6mitvfvvhy5juhmn4afu4tte

Extractive Multi-Document Text Summarization by Using Binary Particle Swarm Optimization

Archana Potnurwar
2020 Bioscience Biotechnology Research Communications  
The absence of a standard dataset and poor work for Hindi text summarization leads to develop a technique for better results.  ...  We have used a combination of Title feature, Sentence length, Sentence position, Numerical Data, Thematic word, Term frequency and Inverse Sentence Frequency for finding the results.  ...  used for minimizing the error which in further reconstruct the original documents. a bottom-up approach is proposed for multi-document summarization to capture the association and order of two textual  ... 
doi:10.21786/bbrc/13.14/8 fatcat:i5joc4qyxjd6lfi7plmobp42n4

Bottom-Up Abstractive Summarization

Sebastian Gehrmann, Yuntian Deng, Alexander Rush
2018 Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing  
We use this selector as a bottom-up attention step to constrain the model to likely phrases. We show that this approach improves the ability to compress text, while still generating fluent summaries.  ...  Furthermore, the content selector can be trained with as little as 1,000 sentences, making it easy to transfer a trained summarizer to a new domain.  ...  Acknowledgements We would like to thank Barbara J. Grosz for helpful discussions and feedback on early stages of this work. We further thank the three anonymous reviewers.  ... 
doi:10.18653/v1/d18-1443 dblp:conf/emnlp/GehrmannDR18 fatcat:e4sptpdsozconn4u3li3ghcswq

Bottom-Up Abstractive Summarization [article]

Sebastian Gehrmann, Yuntian Deng, Alexander M. Rush
2018 arXiv   pre-print
We use this selector as a bottom-up attention step to constrain the model to likely phrases. We show that this approach improves the ability to compress text, while still generating fluent summaries.  ...  Furthermore, the content selector can be trained with as little as 1,000 sentences, making it easy to transfer a trained summarizer to a new domain.  ...  Acknowledgements We would like to thank Barbara J. Grosz for helpful discussions and feedback on early stages of this work. We further thank the three anonymous reviewers.  ... 
arXiv:1808.10792v2 fatcat:sha4wa2uwjflfd5uw3dxegnkv4

Extractive Summarization: Limits, Compression, Generalized Model and Heuristics

Rakesh Verma, Daniel Lee
2018 Journal of Computacion y Sistemas  
Here, we first prove empirical limits on the recall (and F1-scores) of extractive summarizers on the DUC datasets under ROUGE evaluation for both the single-document and multi-document summarization tasks  ...  Due to its promise to alleviate information overload, text summarization has attracted the attention of many researchers. However, it has remained a serious challenge.  ...  Also, we thank the reviewers of CICLING 2017 for their constructive comments.  ... 
doi:10.13053/cys-21-4-2855 fatcat:tdesj4rsqnhphg5ptk4gplvqy4

Automatic Multi Document Summarization Approaches

Kumar
2012 Journal of Computer Science  
With the aim of enhancing multi document summarization, specifically news documents, a novel type of approach is outlined to be developed in the future, taking into account the generic components of a  ...  news story in order to generate a better summary.  ...  In agglomerative clustering (also known as "bottom-up" approach), each sentence is initially considered a separate cluster by its own.  ... 
doi:10.3844/jcssp.2012.133.140 fatcat:6wry32zaufczbkm7f62nor5ipq

Satisfying information needs with multi-document summaries

Sanda Harabagiu, Andrew Hickl, Finley Lacatusu
2007 Information Processing & Management  
Generating summaries that meet the information needs of a user relies on (1) several forms of question decomposition; (2) different summarization approaches; and (3) textual inference for combining the  ...  This novel framework for summarization has the advantage of producing highly responsive summaries, as indicated by the evaluation results.  ...  engines: (1) a question-focused summarization (QFS) system and (2) a multi-document summarization system.  ... 
doi:10.1016/j.ipm.2007.01.004 fatcat:uujzhptylfci3i4kdjknvn7bpa

Extract with Order for Coherent Multi-Document Summarization [article]

Mir Tafseer Nayeem, Yllias Chali
2020 arXiv   pre-print
In this work, we aim at developing an extractive summarizer in the multi-document setting. We implement a rank based sentence selection using continuous vector representations along with key-phrases.  ...  Furthermore, we propose a model to tackle summary coherence for increasing readability. We conduct experiments on the Document Understanding Conference (DUC) 2004 datasets using ROUGE toolkit.  ...  Acknowledgments We would like to thank the anonymous reviewers for their useful comments.  ... 
arXiv:1706.06542v2 fatcat:3o6ochqz5jdhbdk6jc2d44xxyq

Unity in Diversity: Learning Distributed Heterogeneous Sentence Representation for Extractive Summarization [article]

Abhishek Kumar Singh, Manish Gupta, Vasudeva Varma
2019 arXiv   pre-print
Automated multi-document extractive text summarization is a widely studied research problem in the field of natural language understanding.  ...  and compositional aspects latent in a sentence to capture document independent features.  ...  Ren et al. (2016) develop a redundancy aware sentence regression framework for multi-document extractive summarization.  ... 
arXiv:1912.11688v1 fatcat:2337brxyovaqxcge26ry5fsxui

Extract with Order for Coherent Multi-Document Summarization

Mir Tafseer Nayeem, Yllias Chali
2017 Proceedings of TextGraphs-11: the Workshop on Graph-based Methods for Natural Language Processing  
In this work, we aim at developing an extractive summarizer in the multi-document setting. We implement a rank based sentence selection using continuous vector representations along with key-phrases.  ...  Furthermore, we propose a model to tackle summary coherence for increasing readability. We conduct experiments on the Document Understanding Conference (DUC) 2004 datasets using ROUGE toolkit.  ...  Acknowledgments We would like to thank the anonymous reviewers for their useful comments.  ... 
doi:10.18653/v1/w17-2407 dblp:conf/textgraphs/NayeemC17 fatcat:jqflowowdfcd3ij333c53waubq

Review on automatic text summarization

Abirami Rajasekaran, Dr R. Varalakshmi
2018 International Journal of Engineering & Technology  
Due to the abundant information available in different forms of sources and genres, there is an immense need to summarize the data for humans.  ...  Past few years have witnessed a rapid growth in the research of summarizing the text automatically using different approaches.  ...  The Sentences with the top scores are picked up for the summary generation.  ... 
doi:10.14419/ijet.v7i2.33.14210 fatcat:z7lffdxnwnejlgcytgs24va4mq

Transductive learning over automatically detected themes for multi-document summarization

Massih-Reza Amini, Nicolas Usunier
2011 Proceedings of the 34th international ACM SIGIR conference on Research and development in Information - SIGIR '11  
We propose a new method for query-biased multi-document summarization, based on sentence extraction. The summary of multiple documents is created in two steps.  ...  Inside each theme, sentences are then ranked using a transductive learning-torank algorithm based on RankNet [2], in order to better identify those which are relevant to the query.  ...  CONCLUSION We proposed a learning to rank approach for extractive summarization based on a transductive setting.  ... 
doi:10.1145/2009916.2010115 dblp:conf/sigir/AminiU11 fatcat:euoccg5iirgsnigtpheeaxz2jm
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