用人类沟通模型提升摘要对问题的回答能力
Does This Summary Answer My Question? Modeling Query-Focused Summary Readers with Rational Speech Acts
- 引入理性言语行为框架，模拟读者对摘要的理解过程
- 通过答案重建目标重排摘要，提升与查询的匹配度
- 适合改进用户导向型生成系统，无需复杂结构调整
查询聚焦摘要（QFS）是根据用户提问生成摘要的任务。尽管其以用户为中心，但现有研究很少显式考虑用户对生成摘要的理解，可能导致推理时表现不佳。本文引入理性言语行为（RSA）框架，建模读者对查询聚焦摘要的理解，并融入现有QFS系统的生成方法。我们提出答案重建目标，通过评估读者利用摘要重构初始问题答案的能力来近似其理解程度。基于该目标，可对现有QFS系统生成的候选摘要进行重排序，选出更契合查询和参考摘要的版本。研究表明，将用户需求显式纳入生成流程，是提升面向用户的语言生成系统性能的一种简单有效方式。
原文摘要 · Abstract (English)
Query-focused summarization (QFS) is the task of generating a summary in response to a user-written query. Despite its user-oriented nature, there has been limited work in QFS in explicitly considering a user's understanding of a generated summary, potentially causing QFS systems to underperform at inference time. In this paper, we adapt the Rational Speech Act (RSA) framework, a model of human communication, to explicitly model a reader's understanding of a query-focused summary and integrate it within the generation method of existing QFS systems. In particular, we introduce the answer reconstruction objective which approximates a reader's understanding of a summary by their ability to use it to reconstruct the answer to their initial query. Using this objective, we are able to re-rank candidate summaries generated by existing QFS systems and select summaries that better align with their corresponding query and reference summary. More generally, our study suggests that a simple and effective way of improving a language generation system designed for a user-centered task may be to explicitly incorporate its user requirements into the system's generation procedure.
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