Abstract

The objective of our research is to produce summaries of films from collateral text which capture the semantic content and narrative structure and key lines of dialogue in film. Our hypothesis is that text summaries of movies can efficiently be produced by the use of text summarisation techniques on collateral texts: subtitles, audio descriptions and post-production screenplays. If time codings are available, then video summaries can be constructed from these text summaries. In this initial study, we form summaries by selecting the ten shots from the original film which contain the highest proportion of keywords. We define keywords in two ways: as mid-frequency words, since it is the mid-frequency words in a text which contain most information about the content of that text, and as named entities derived from the cast list of the film. Ye et al. (2007) argued that the quality of a summary can be evaluated based on the how many concepts in the original text are preserved after summarisation. We found that this approach to summary evaluation gave more favourable results than the ROUGE approach (Lin and Franz, 2004) of comparing the number of matching character sequences in an automatic and a human-generated summary.