Adapting Neural Single-Document Summarization Model for Abstractive Multi-Document Summarization: A Pilot Study

Jianmin Zhang, Jiwei Tan, Xiaojun Wan


Abstract
Till now, neural abstractive summarization methods have achieved great success for single document summarization (SDS). However, due to the lack of large scale multi-document summaries, such methods can be hardly applied to multi-document summarization (MDS). In this paper, we investigate neural abstractive methods for MDS by adapting a state-of-the-art neural abstractive summarization model for SDS. We propose an approach to extend the neural abstractive model trained on large scale SDS data to the MDS task. Our approach only makes use of a small number of multi-document summaries for fine tuning. Experimental results on two benchmark DUC datasets demonstrate that our approach can outperform a variety of baseline neural models.
Anthology ID:
W18-6545
Volume:
Proceedings of the 11th International Conference on Natural Language Generation
Month:
November
Year:
2018
Address:
Tilburg University, The Netherlands
Venue:
INLG
SIG:
SIGGEN
Publisher:
Association for Computational Linguistics
Note:
Pages:
381–390
Language:
URL:
https://aclanthology.org/W18-6545
DOI:
10.18653/v1/W18-6545
Bibkey:
Cite (ACL):
Jianmin Zhang, Jiwei Tan, and Xiaojun Wan. 2018. Adapting Neural Single-Document Summarization Model for Abstractive Multi-Document Summarization: A Pilot Study. In Proceedings of the 11th International Conference on Natural Language Generation, pages 381–390, Tilburg University, The Netherlands. Association for Computational Linguistics.
Cite (Informal):
Adapting Neural Single-Document Summarization Model for Abstractive Multi-Document Summarization: A Pilot Study (Zhang et al., INLG 2018)
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PDF:
https://preview.aclanthology.org/starsem-semeval-split/W18-6545.pdf