The automatic construction of large-scale corpora for summarization research

Summarization research is notorious for its lack of adequat e corpora: today, there exist only a few small collections of texts whose units have been manually annotated for textual importance. Given the cost and tediousness of the annotation process, it is very unlikely that we will ever manually annotate for textual importance sufficiently large corpora f texts. To circumvent this problem, we have developed an algorithm that constructs such corpora automatically. Our algorithm takes as input an hAbstract, Text i tuple and generates the corresponding Extract, i.e., the set of clauses (sentences) in the Text that were used to write the Abstract. The performance of the algorithm is shown to be close to that of humans by means of an empirical experiment. The experiment also suggests extraction strategies that could impro ve the performance of automatic summarization systems.

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