A Comparative Study of Deep Learning Approaches for Query-Focused Extractive Multi-Document Summarization

Yuliska, Tetsuya Sakai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Query-focused multi-document summarization aims to produce a single, short document that summarizes a set of documents that are relevant to a given query. During the past few years, deep learning approaches have been utilized to generate summaries in an abstractive or extractive manner. In this study, we employ six deep neural network approaches to solving a query-focused extractive multi-document summarization task and compare their performances. To the best of our knowledge, our study is the first to compare deep learning techniques on extractive query-focused multi-document summarization. Our experiments with DUC 2005-2007 benchmark datasets show that Bi-LSTM with Max-pooling achieves the highest performance among the methods compared.

Original languageEnglish
Title of host publication2019 IEEE 2nd International Conference on Information and Computer Technologies, ICICT 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages153-157
Number of pages5
ISBN (Electronic)9781728133232
DOIs
Publication statusPublished - 2019 May 9
Event2nd IEEE International Conference on Information and Computer Technologies, ICICT 2019 - Kahului, United States
Duration: 2019 Mar 142019 Mar 17

Publication series

Name2019 IEEE 2nd International Conference on Information and Computer Technologies, ICICT 2019

Conference

Conference2nd IEEE International Conference on Information and Computer Technologies, ICICT 2019
CountryUnited States
CityKahului
Period19/3/1419/3/17

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Keywords

  • comparative study
  • deep neural network
  • extractive summarization
  • query-focused summarization
  • text summarization

ASJC Scopus subject areas

  • Artificial Intelligence
  • Social Sciences (miscellaneous)
  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems
  • Information Systems and Management

Cite this

Yuliska, & Sakai, T. (2019). A Comparative Study of Deep Learning Approaches for Query-Focused Extractive Multi-Document Summarization. In 2019 IEEE 2nd International Conference on Information and Computer Technologies, ICICT 2019 (pp. 153-157). [8710851] (2019 IEEE 2nd International Conference on Information and Computer Technologies, ICICT 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/INFOCT.2019.8710851