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

Yuliska, Tetsuya Sakai

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトル2019 IEEE 2nd International Conference on Information and Computer Technologies, ICICT 2019
出版社Institute of Electrical and Electronics Engineers Inc.
ページ153-157
ページ数5
ISBN(電子版)9781728133232
DOI
出版ステータスPublished - 2019 5 9
イベント2nd IEEE International Conference on Information and Computer Technologies, ICICT 2019 - Kahului, United States
継続期間: 2019 3 142019 3 17

出版物シリーズ

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

Conference

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

ASJC Scopus subject areas

  • 人工知能
  • 社会科学(その他)
  • コンピュータ ネットワークおよび通信
  • コンピュータ サイエンスの応用
  • 情報システム
  • 情報システムおよび情報管理

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