Speech-to-text summarization using automatic phrase extraction from recognized text

Abstract

This paper describes a summarization system that was developed in order to summarize news delivered orally. The system generates text summaries from input audio using three independent components: an automatic speech recognizer, a syntactic analyzer, and a summarizer. The absence of sentence boundaries in the recognized text complicates the summarization process. Therefore, we use a syntactic analyzer to identify continuous segments in the recognized text. We used 50 reference articles to perform our evaluation. The data are publicly available at http://nlp.ite.tul.cz/sumarizace. The results of the proposed system were compared with the results of sentence summarization in the reference articles. The evaluation was performed using co-occurrence of n-grams in the reference and generated summaries, and by readers mark-ups. The readers marked two aspects of the summaries: readability and information relevance. Experiments confirm that the generated summaries have the same information value as the reference summaries. However, readers state that phrase summaries are hard to read without the whole sentence context.