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
Editors:
Mohit Bansal, Aline Villavicencio
Venue:
CoNLL
SIG:
SIGNLL
Publisher:
Association for Computational Linguistics
Note:
Pages:
833–842
Language:
URL:
https://aclanthology.org/K19-1078
DOI:
10.18653/v1/K19-1078
Bibkey:
Cite (ACL):
Kushal Chawla, Balaji Vasan Srinivasan, and Niyati Chhaya. 2019. Generating Formality-Tuned Summaries Using Input-Dependent Rewards. In Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), pages 833–842, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Generating Formality-Tuned Summaries Using Input-Dependent Rewards (Chawla et al., CoNLL 2019)
Copy Citation:
PDF:
https://aclanthology.org/K19-1078.pdf
Supplementary material:
 K19-1078.Supplementary_Material.zip