Abstractive news summarization based on event semantic link network

Wei Li; Lei He; Hai Zhuge

Abstractive news summarization based on event semantic link network

Wei Li, Lei He, Hai Zhuge

Research output: Chapter in Book/Published conference output › Conference publication

Abstract

This paper studies the abstractive multi-document summarization for event-oriented news texts through event information extraction and abstract representation. Fine-grained event mentions and semantic relations between them are extracted to build a unified and connected event semantic link network, an abstract representation of source texts. A network reduction algorithm is proposed to summarize the most salient and coherent event information. New sentences with good linguistic quality are automatically generated and selected through sentences over-generation and greedy-selection processes. Experimental results on DUC2006 and DUC2007 datasets show that our system significantly outperforms the state-of-the-art extractive and abstractive baselines under both pyramid and ROUGE evaluation metrics.

Original language	English
Title of host publication	The 26th International Conference on Computational Linguistics
Publisher	Association for Computational Linguistics
Pages	1-11
ISBN (Print)	978-4-87974-702-0
Publication status	Published - 11 Dec 2016
Event	26th International Conference on Computational Linguistics: COLIN 2016 - Osaka, Japan Duration: 11 Dec 2016 → 16 Dec 2016 Conference number: 26

Conference

Conference	26th International Conference on Computational Linguistics
Abbreviated title	COLING 2016
Country/Territory	Japan
City	Osaka
Period	11/12/16 → 16/12/16

Bibliographical note

This work is licenced under a Creative Commons Attribution 4.0 International License. License details: http://
creativecommons.org/licenses/by/4.0/

Keywords

Summarization
sematnic link network
event

Access to Document

Abstractive News Summarization based on Semantic Link Network
This work is licenced under a Creative Commons Attribution 4.0 International License. License details: http:// creativecommons.org/licenses/by/4.0/
Accepted author manuscript, 362 KBLicence: CC BY 3.0

Cite this

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title = "Abstractive news summarization based on event semantic link network",

abstract = "This paper studies the abstractive multi-document summarization for event-oriented news texts through event information extraction and abstract representation. Fine-grained event mentions and semantic relations between them are extracted to build a unified and connected event semantic link network, an abstract representation of source texts. A network reduction algorithm is proposed to summarize the most salient and coherent event information. New sentences with good linguistic quality are automatically generated and selected through sentences over-generation and greedy-selection processes. Experimental results on DUC2006 and DUC2007 datasets show that our system significantly outperforms the state-of-the-art extractive and abstractive baselines under both pyramid and ROUGE evaluation metrics.",

keywords = "Summarization, sematnic link network, event",

author = "Wei Li and Lei He and Hai Zhuge",

note = "This work is licenced under a Creative Commons Attribution 4.0 International License. License details: http:// creativecommons.org/licenses/by/4.0/; 26th International Conference on Computational Linguistics : COLIN 2016, COLING 2016 ; Conference date: 11-12-2016 Through 16-12-2016",

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TY - GEN

T1 - Abstractive news summarization based on event semantic link network

AU - Li, Wei

AU - He, Lei

AU - Zhuge, Hai

N1 - Conference code: 26

PY - 2016/12/11

Y1 - 2016/12/11

N2 - This paper studies the abstractive multi-document summarization for event-oriented news texts through event information extraction and abstract representation. Fine-grained event mentions and semantic relations between them are extracted to build a unified and connected event semantic link network, an abstract representation of source texts. A network reduction algorithm is proposed to summarize the most salient and coherent event information. New sentences with good linguistic quality are automatically generated and selected through sentences over-generation and greedy-selection processes. Experimental results on DUC2006 and DUC2007 datasets show that our system significantly outperforms the state-of-the-art extractive and abstractive baselines under both pyramid and ROUGE evaluation metrics.

AB - This paper studies the abstractive multi-document summarization for event-oriented news texts through event information extraction and abstract representation. Fine-grained event mentions and semantic relations between them are extracted to build a unified and connected event semantic link network, an abstract representation of source texts. A network reduction algorithm is proposed to summarize the most salient and coherent event information. New sentences with good linguistic quality are automatically generated and selected through sentences over-generation and greedy-selection processes. Experimental results on DUC2006 and DUC2007 datasets show that our system significantly outperforms the state-of-the-art extractive and abstractive baselines under both pyramid and ROUGE evaluation metrics.

KW - Summarization

KW - sematnic link network

KW - event

UR - https://aclanthology.coli.uni-saarland.de/volumes/proceedings-of-coling-2016-the-26th-international-conference-on-computational-linguistics-technical-papers

UR - http://coling2016.anlp.jp/

M3 - Conference publication

SN - 978-4-87974-702-0

SP - 1

EP - 11

BT - The 26th International Conference on Computational Linguistics

PB - Association for Computational Linguistics

T2 - 26th International Conference on Computational Linguistics

Y2 - 11 December 2016 through 16 December 2016

ER -

Abstractive news summarization based on event semantic link network

Abstract

Conference

Bibliographical note

Keywords

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