Extractive Dialogue Summarization Without Annotation Based on Distantly Supervised Machine Reading Comprehension in Customer Service

Given a long dialogue, the dialogue summarization system aims to obtain a shorter highlight which retains the important information in the original text. For the customer service scenarios, the summaries of most dialogues between an agent and a user focus on several fixed key points, such as users’ question, users’ 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 the annotation for key points. Moreover, the speaker’s role information is ignored or not fully utilized in previous work. 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. What’s more, a speaker’s role token and the solver classification task are proposed to make full use of speaker’s role information. We conduct experiments on a real-world summarization dataset collected in customer service scenarios, and the results show that the proposed method outperforms the strong baseline methods by 6 percentage points on ROUGE $_L$ .