提出无监督聚类方法，提升会议语音自动摘要效果
Team MTS @ AutoMin 2021: An Overview of Existing Summarization Approaches and Comparison to Unsupervised Summarization Techniques
- 用聚类实现无监督摘要，不依赖标注数据
- 在开发集上Rouge-L达0.2，测试集上摘要质量评分为1.857
- 适合真实录音场景，无需预训练模型
疫情期间远程视频会议激增，催生了自动会议纪要系统的需求。本文介绍团队MTS参与自动纪要挑战赛的研究成果：分析现有文本与语音摘要方法，提出一种基于聚类的无监督摘要技术，并构建包含改进语音识别模块的端到端流程，可处理真实录音。该方法在开发集上获得Rouge-1为0.21、Rouge-2为0.02、Rouge-L为0.2的成绩；在测试集上，Rouge-1、Rouge-2、Rouge-L、适切性、语法正确性与流畅性得分分别为0.180、0.035、0.098、1.857、2.304、1.911，优于预训练摘要模型。
原文摘要 · Abstract (English)
Remote communication through video or audio conferences has become more popular than ever because of the worldwide pandemic. These events, therefore, have provoked the development of systems for automatic minuting of spoken language leading to AutoMin 2021 challenge. The following paper illustrates the results of the research that team MTS has carried out while participating in the Automatic Minutes challenge. In particular, in this paper we analyze existing approaches to text and speech summarization, propose an unsupervised summarization technique based on clustering and provide a pipeline that includes an adapted automatic speech recognition block able to run on real-life recordings. The proposed unsupervised technique outperforms pre-trained summarization models on the automatic minuting task with Rouge 1, Rouge 2 and Rouge L values of 0.21, 0.02 and 0.2 on the dev set, with Rouge 1, Rouge 2, Rouge L, Adequacy, Grammatical correctness and Fluency values of 0.180, 0.035, 0.098, 1.857, 2.304, 1.911 on the test set accordingly
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