Entity Commonsense Representation for Neural Abstractive Summarization

Reinald Kim Amplayo, Seonjae Lim, Seung-won Hwang


Abstract
A major proportion of a text summary includes important entities found in the original text. These entities build up the topic of the summary. Moreover, they hold commonsense information once they are linked to a knowledge base. Based on these observations, this paper investigates the usage of linked entities to guide the decoder of a neural text summarizer to generate concise and better summaries. To this end, we leverage on an off-the-shelf entity linking system (ELS) to extract linked entities and propose Entity2Topic (E2T), a module easily attachable to a sequence-to-sequence model that transforms a list of entities into a vector representation of the topic of the summary. Current available ELS’s are still not sufficiently effective, possibly introducing unresolved ambiguities and irrelevant entities. We resolve the imperfections of the ELS by (a) encoding entities with selective disambiguation, and (b) pooling entity vectors using firm attention. By applying E2T to a simple sequenceto-sequence model with attention mechanism as base model, we see significant improvements of the performance in the Gigaword (sentence to title) and CNN (long document to multi-sentence highlights) summarization datasets by at least 2 ROUGE points.
Anthology ID:
N18-1064
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
697–707
Language:
URL:
https://aclanthology.org/N18-1064
DOI:
10.18653/v1/N18-1064
Bibkey:
Cite (ACL):
Reinald Kim Amplayo, Seonjae Lim, and Seung-won Hwang. 2018. Entity Commonsense Representation for Neural Abstractive Summarization. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 697–707, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
Entity Commonsense Representation for Neural Abstractive Summarization (Amplayo et al., NAACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/N18-1064.pdf
Video:
 http://vimeo.com/282319318
Code
 rktamplayo/Entity2Topic