Generating Formality-Tuned Summaries Using Input-Dependent Rewards

Kushal Chawla, Balaji Vasan Srinivasan, Niyati Chhaya


Abstract
Abstractive text summarization aims at generating human-like summaries by understanding and paraphrasing the given input content. Recent efforts based on sequence-to-sequence networks only allow the generation of a single summary. However, it is often desirable to accommodate the psycho-linguistic preferences of the intended audience while generating the summaries. In this work, we present a reinforcement learning based approach to generate formality-tailored summaries for an input article. Our novel input-dependent reward function aids in training the model with stylistic feedback on sampled and ground-truth summaries together. Once trained, the same model can generate formal and informal summary variants. Our automated and qualitative evaluations show the viability of the proposed framework.
Anthology ID:
K19-1078
Volume:
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venue:
CoNLL
SIG:
SIGNLL
Publisher:
Association for Computational Linguistics
Note:
Pages:
833–842
Language:
URL:
https://www.aclweb.org/anthology/K19-1078
DOI:
10.18653/v1/K19-1078
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/K19-1078.pdf
Supplementary material:
 K19-1078.Supplementary_Material.zip