Distant Supervision based Machine Reading Comprehension for Extractive Summarization in Customer Service

Abstract

Given a long text, the summarization system aims to obtain a shorter highlight while keeping important information on the original text. For customer service, the summaries of most dialogues between an agent and a user focus on several fixed key points, such as user's question, user's purpose, the agent's solution, and so on. Traditional extractive methods are difficult to extract all predefined key points exactly. Furthermore, there is a lack of large-scale and high-quality extractive summarization datasets containing key points. In order to solve the above challenges, we propose a Distant Supervision based Machine Reading Comprehension model for extractive Summarization (DSMRC-S). DSMRC-S transforms the summarization task into the machine reading comprehension problem, to fetch key points from the original text exactly according to the predefined questions. In addition, a distant supervision method is proposed to alleviate the lack of eligible extractive summarization datasets. We conduct experiments on a large-scale summarization dataset collected in customer service scenarios, and the results show that the proposed DSMRC-S outperforms the strong baseline methods by 4 points on ROUGE-L.