\'Etude de l'informativit\'e des transcriptions : une approche bas\'ee sur le r\'esum\'e automatique

TL;DR

This paper introduces a new method to evaluate the informativeness of automatic speech recognition transcriptions by analyzing their summaries and divergence in information content, assessing both transcription quality and summarization effectiveness.

Contribution

It presents a novel approach combining informativeness estimation and automatic summarization to evaluate and improve speech transcription quality.

Findings

Automatic summarization can mitigate information loss in transcriptions.

The divergence measure effectively assesses transcription informativeness.

The method provides insights into transcription quality and summarization performance.

Abstract

In this paper we propose a new approach to evaluate the informativeness of transcriptions coming from Automatic Speech Recognition systems. This approach, based in the notion of informativeness, is focused on the framework of Automatic Text Summarization performed over these transcriptions. At a first glance we estimate the informative content of the various automatic transcriptions, then we explore the capacity of Automatic Text Summarization to overcome the informative loss. To do this we use an automatic summary evaluation protocol without reference (based on the informative content), which computes the divergence between probability distributions of different textual representations: manual and automatic transcriptions and their summaries. After a set of evaluations this analysis allowed us to judge both the quality of the transcriptions in terms of informativeness and to assess the ability of automatic text summarization to compensate the problems raised during the transcription phase.