Team MTS @ AutoMin 2021: An Overview of Existing Summarization Approaches and Comparison to Unsupervised Summarization Techniques

TL;DR

This paper reviews existing summarization methods, introduces an unsupervised clustering-based approach for text and speech summarization, and demonstrates its superior performance over pre-trained models in the AutoMin 2021 challenge.

Contribution

It presents a novel unsupervised summarization technique using clustering, combined with an adapted speech recognition pipeline for real-life recordings.

Findings

Unsupervised clustering-based summarization outperforms pre-trained models on the AutoMin dataset.

The proposed pipeline achieves competitive Rouge scores and improved fluency and grammatical correctness.

The approach is effective for automatic minuting of spoken language in real-world scenarios.

Abstract

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