Adapting the Neural Encoder-Decoder Framework from Single to Multi-Document Summarization

Logan Lebanoff, Kaiqiang Song, Fei Liu


Abstract
Generating a text abstract from a set of documents remains a challenging task. The neural encoder-decoder framework has recently been exploited to summarize single documents, but its success can in part be attributed to the availability of large parallel data automatically acquired from the Web. In contrast, parallel data for multi-document summarization are scarce and costly to obtain. There is a pressing need to adapt an encoder-decoder model trained on single-document summarization data to work with multiple-document input. In this paper, we present an initial investigation into a novel adaptation method. It exploits the maximal marginal relevance method to select representative sentences from multi-document input, and leverages an abstractive encoder-decoder model to fuse disparate sentences to an abstractive summary. The adaptation method is robust and itself requires no training data. Our system compares favorably to state-of-the-art extractive and abstractive approaches judged by automatic metrics and human assessors.
Anthology ID:
D18-1446
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Editors:
Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
4131–4141
Language:
URL:
https://aclanthology.org/D18-1446
DOI:
10.18653/v1/D18-1446
Bibkey:
Cite (ACL):
Logan Lebanoff, Kaiqiang Song, and Fei Liu. 2018. Adapting the Neural Encoder-Decoder Framework from Single to Multi-Document Summarization. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4131–4141, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Adapting the Neural Encoder-Decoder Framework from Single to Multi-Document Summarization (Lebanoff et al., EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1446.pdf
Code
 ucfnlp/multidoc_summarization