Generating Formality-Tuned Summaries Using Input-Dependent Rewards
Kushal Chawla, Balaji Vasan Srinivasan, Niyati Chhaya · 2019
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.