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:
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/C16-1023.pdf