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arXiv · 2109.06853

Summarize-then-Answer: Generating Concise Explanations for Multi-hop Reading Comprehension

Naoya Inoue·Harsh Trivedi·Steven Sinha·Niranjan Balasubramanian·Kentaro Inui
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Abstract

How can we generate concise explanations for multi-hop Reading Comprehension (RC)? The current strategies of identifying supporting sentences can be seen as an extractive question-focused summarization of the input text. However, these extractive explanations are not necessarily concise i.e. not minimally sufficient for answering a question. Instead, we advocate for an abstractive approach, where we propose to generate a question-focused, abstractive summary of input paragraphs and then feed it to an RC system. Given a limited amount of human-annotated abstractive explanations, we train the abstractive explainer in a semi-supervised manner, where we start from the supervised model and then train it further through trial and error maximizing a conciseness-promoted reward function. Our experiments demonstrate that the proposed abstractive explainer can generate more compact explanations than an extractive explainer with limited supervision (only 2k instances) while maintaining sufficiency.

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Naoya Inoue, Harsh Trivedi, Steven Sinha, Niranjan Balasubramanian, Kentaro Inui. 2021-09-14. Summarize-then-Answer: Generating Concise Explanations for Multi-hop Reading Comprehension. https://arxiv.org/abs/2109.06853

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