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 language | English |
|---|---|
| Title of host publication | 2019 IEEE 2nd International Conference on Information and Computer Technologies, ICICT 2019 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 153-157 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781728133232 |
| DOIs | |
| Publication status | Published - 2019 May 9 |
| Event | 2nd IEEE International Conference on Information and Computer Technologies, ICICT 2019 - Kahului, United States Duration: 2019 Mar 14 → 2019 Mar 17 |
Publication series
| Name | 2019 IEEE 2nd International Conference on Information and Computer Technologies, ICICT 2019 |
|---|
Conference
| Conference | 2nd IEEE International Conference on Information and Computer Technologies, ICICT 2019 |
|---|---|
| Country | United States |
| City | Kahului |
| Period | 19/3/14 → 19/3/17 |
Fingerprint
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
A Comparative Study of Deep Learning Approaches for Query-Focused Extractive Multi-Document Summarization. / Yuliska, ; Sakai, Tetsuya.
2019 IEEE 2nd International Conference on Information and Computer Technologies, ICICT 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 153-157 8710851 (2019 IEEE 2nd International Conference on Information and Computer Technologies, ICICT 2019).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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TY - GEN
T1 - A Comparative Study of Deep Learning Approaches for Query-Focused Extractive Multi-Document Summarization
AU - Yuliska,
AU - Sakai, Tetsuya
PY - 2019/5/9
Y1 - 2019/5/9
N2 - 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.
AB - 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.
KW - comparative study
KW - deep neural network
KW - extractive summarization
KW - query-focused summarization
KW - text summarization
UR - http://www.scopus.com/inward/record.url?scp=85066608855&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85066608855&partnerID=8YFLogxK
U2 - 10.1109/INFOCT.2019.8710851
DO - 10.1109/INFOCT.2019.8710851
M3 - Conference contribution
T3 - 2019 IEEE 2nd International Conference on Information and Computer Technologies, ICICT 2019
SP - 153
EP - 157
BT - 2019 IEEE 2nd International Conference on Information and Computer Technologies, ICICT 2019
PB - Institute of Electrical and Electronics Engineers Inc.
ER -