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        <title>Generating Formality-Tuned Summaries Using Input-Dependent Rewards</title>
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        <namePart type="given">Vasan</namePart>
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    <name type="personal">
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            <title>Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T Generating Formality-Tuned Summaries Using Input-Dependent Rewards
%A Chawla, Kushal
%A Srinivasan, Balaji Vasan
%A Chhaya, Niyati
%Y Bansal, Mohit
%Y Villavicencio, Aline
%S Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F chawla-etal-2019-generating
%X 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.
%R 10.18653/v1/K19-1078
%U https://aclanthology.org/K19-1078
%U https://doi.org/10.18653/v1/K19-1078
%P 833-842

Markdown (Informal)

[Generating Formality-Tuned Summaries Using Input-Dependent Rewards](https://aclanthology.org/K19-1078) (Chawla et al., CoNLL 2019)

• Generating Formality-Tuned Summaries Using Input-Dependent Rewards (Chawla et al., CoNLL 2019)

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.