Definition: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
Input: We assess the performance of generic text summarization algorithms applied to films and documentaries, using extracts from news articles produced by reference models of extractive summarization. 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. c © 2016 Elsevier Ltd. All rights reserved.

Summarization of Films and Documentaries Based on Subtitles and Scripts