Abstractive News Summarization based on Event Semantic Link Network

Wei Li, Lei He, Hai Zhuge


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 DUC 2006 and DUC 2007 datasets show that our system significantly outperforms the state-of-the-art extractive and abstractive baselines under both pyramid and ROUGE evaluation metrics.
Anthology ID:
C16-1023
Volume:
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
Month:
December
Year:
2016
Address:
Osaka, Japan
Editors:
Yuji Matsumoto, Rashmi Prasad
Venue:
COLING
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
236–246
Language:
URL:
https://aclanthology.org/C16-1023
DOI:
Bibkey:
Cite (ACL):
Wei Li, Lei He, and Hai Zhuge. 2016. Abstractive News Summarization based on Event Semantic Link Network. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 236–246, Osaka, Japan. The COLING 2016 Organizing Committee.
Cite (Informal):
Abstractive News Summarization based on Event Semantic Link Network (Li et al., COLING 2016)
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PDF:
https://aclanthology.org/C16-1023.pdf