Summarization of Films and Documentaries Based on Subtitles and Scripts
Table 7: ROUGE scores for generated summaries for subtitles, scripts, and scripts concatenated with subtitles, against plot summaries. For MMR, λ=0.50; Support Sets used the cosine distance and threshold = 50%; KP-Centrality used 50 key phrases.
['MMR', 'Subtitles', 'R-1 0.07', 'R-2 0.01', 'R-SU4 0.02', 'AVG #Words 52']
Overall, Support Sets, LSA, and LexRank, capture the most relevant sentences for plot summaries. It would be expected, for algorithms such as GRASSHOPPER and MMR, that maximize diversity, to perform well in this context, because plot summaries are relatively small and focus on the more important aspects of the film, ideally, without redundant content. However, our results show otherwise. For scripts, LSA and LexRank are the best approaches in terms of R-1 and R-SU4.