Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards

• URL: http://arxiv.org/abs/2107.00176v1
• Date: Thu, 1 Jul 2021 02:06:46 GMT
• Title: Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards
• Authors: Shweta Yadav, Deepak Gupta, Asma Ben Abacha and Dina Demner-Fushman
• Abstract summary: We introduce a reinforcement learning-based framework for abstractive question summarization. We propose two novel rewards obtained from the downstream tasks of (i) question-type identification and (ii) question-focus recognition. These rewards ensure the generation of semantically valid questions and encourage the inclusion of key medical entities/foci in the question summary.
• Score: 20.342580435464072
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The growth of online consumer health questions has led to the necessity for reliable and accurate question answering systems. A recent study showed that manual summarization of consumer health questions brings significant improvement in retrieving relevant answers. However, the automatic summarization of long questions is a challenging task due to the lack of training data and the complexity of the related subtasks, such as the question focus and type recognition. In this paper, we introduce a reinforcement learning-based framework for abstractive question summarization. We propose two novel rewards obtained from the downstream tasks of (i) question-type identification and (ii) question-focus recognition to regularize the question generation model. These rewards ensure the generation of semantically valid questions and encourage the inclusion of key medical entities/foci in the question summary. We evaluated our proposed method on two benchmark datasets and achieved higher performance over state-of-the-art models. The manual evaluation of the summaries reveals that the generated questions are more diverse and have fewer factual inconsistencies than the baseline summaries

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