TY - GEN
T1 - A Comparative Study of Deep Learning Approaches for Query-Focused Extractive Multi-Document Summarization
AU - Yuliska,
AU - Sakai, Tetsuya
N1 - Funding Information:
ACKNOWLEDGMENT This study was undertaken with the support from Indonesia Endowment Fund for Education (LPDP).
Publisher Copyright:
© 2019 IEEE.
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
AN - SCOPUS:85066608855
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.
T2 - 2nd IEEE International Conference on Information and Computer Technologies, ICICT 2019
Y2 - 14 March 2019 through 17 March 2019
ER -