Étude de l'informativité des transcriptions : une approche basée sur le résumé automatique
This work addresses the quality assessment of transcriptions for speech recognition users, but it is incremental as it applies existing summarization methods to a new evaluation context.
The paper tackles the problem of evaluating the informativeness of automatic speech recognition transcriptions by using automatic text summarization, finding that summarization can compensate for informative loss in transcriptions through divergence-based evaluations.
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.