selection and ranking of text from highly imperfect transcripts for retrieval of video content
In the domain of video content retrieval, we present an approach for selecting words and phrases from highly imperfect automatically generated transcripts. Extracted terms are ranked according to their descriptiveness and presented to the user in a multimedia browser interface. We use sense querying from the WordNet lexical database for our method of text selection and ranking. Evaluation of 679 video summarization tasks from 442 users shows that the method of ranking and emphasizing terms according to descriptiveness results in higher accuracy responses in less time compared to the baseline of no ranking.
ranking imperfect transcription relevancy speech transcripts