Summarization of Films and Documentaries Based on Subtitles and Scripts
This work addresses the problem of summarizing visual media content for researchers and developers, but it is incremental as it applies existing methods to new data without introducing novel techniques.
The study evaluated generic text summarization algorithms on films and documentaries using news articles as a reference, finding that LSA performed best for news and documentaries, while LexRank and Support Sets were top for films, with ROUGE metrics showing consistent behavior across these media types.
We assess the performance of generic text summarization algorithms applied to films and documentaries, using the well-known behavior of summarization of news articles as reference. We use three datasets: (i) news articles, (ii) film scripts and subtitles, and (iii) documentary subtitles. Standard ROUGE metrics are used for comparing generated summaries against news abstracts, plot summaries, and synopses. We show that the best performing algorithms are LSA, for news articles and documentaries, and LexRank and Support Sets, for films. Despite the different nature of films and documentaries, their relative behavior is in accordance with that obtained for news articles.