Vocabulary Tailored Summary Generation

Kundan Krishna, Aniket Murhekar, Saumitra Sharma, Balaji Vasan Srinivasan


Abstract
Neural sequence-to-sequence models have been successfully extended for summary generation. However, existing frameworks generate a single summary for a given input and do not tune the summaries towards any additional constraints/preferences. Such a tunable framework is desirable to account for linguistic preferences of the specific audience who will consume the summary. In this paper, we propose a neural framework to generate summaries constrained to a vocabulary-defined linguistic preferences of a target audience. The proposed method accounts for the generation context by tuning the summary words at the time of generation. Our evaluations indicate that the proposed approach tunes summaries to the target vocabulary while still maintaining a superior summary quality against a state-of-the-art word embedding based lexical substitution algorithm, suggesting the feasibility of the proposed approach. We demonstrate two applications of the proposed approach - to generate understandable summaries with simpler words, and readable summaries with shorter words.
Anthology ID:
C18-1068
Volume:
Proceedings of the 27th International Conference on Computational Linguistics
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico, USA
Editors:
Emily M. Bender, Leon Derczynski, Pierre Isabelle
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
795–805
Language:
URL:
https://aclanthology.org/C18-1068
DOI:
Bibkey:
Cite (ACL):
Kundan Krishna, Aniket Murhekar, Saumitra Sharma, and Balaji Vasan Srinivasan. 2018. Vocabulary Tailored Summary Generation. In Proceedings of the 27th International Conference on Computational Linguistics, pages 795–805, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
Cite (Informal):
Vocabulary Tailored Summary Generation (Krishna et al., COLING 2018)
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PDF:
https://aclanthology.org/C18-1068.pdf