Controllable Abstractive Sentence Summarization with Guiding Entities

Changmeng Zheng, Yi Cai, Guanjie Zhang, Qing Li

Abstract
Entities are the major proportion and build up the topic of text summaries. Although existing text summarization models can produce promising results of automatic metrics, for example, ROUGE, it is difficult to guarantee that an entity is contained in generated summaries. In this paper, we propose a controllable abstractive sentence summarization model which generates summaries with guiding entities. Instead of generating summaries from left to right, we start with a selected entity, generate the left part first, then the right part of a complete summary. Compared to previous entity-based text summarization models, our method can ensure that entities appear in final output summaries rather than generating the complete sentence with implicit entity and article representations. Our model can also generate more novel entities with them incorporated into outputs directly. To evaluate the informativeness of the proposed model, we develop a fine-grained informativeness metrics in the relevance, extraness and omission perspectives. We conduct experiments in two widely-used sentence summarization datasets and experimental results show that our model outperforms the state-of-the-art methods in both automatic evaluation scores and informativeness metrics.
Anthology ID:
2020.coling-main.497
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Editors:
Donia Scott, Nuria Bel, Chengqing Zong
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
5668–5678
Language:
URL:
https://aclanthology.org/2020.coling-main.497
DOI:
10.18653/v1/2020.coling-main.497
Bibkey:
Cite (ACL):
Changmeng Zheng, Yi Cai, Guanjie Zhang, and Qing Li. 2020. Controllable Abstractive Sentence Summarization with Guiding Entities. In Proceedings of the 28th International Conference on Computational Linguistics, pages 5668–5678, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
Controllable Abstractive Sentence Summarization with Guiding Entities (Zheng et al., COLING 2020)
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PDF:
https://preview.aclanthology.org/nschneid-patch-4/2020.coling-main.497.pdf
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