An Approach to Summarizing Short Stories

By Anna Kazantseva. 2006

14 citations recorded by the source corpus.

This paper describes a system that produces extractive summaries of short works of literary fiction. The ultimate purpose of produced summaries is defined as helping a reader to determine whether she would be interested in reading a particular story. To this end, the summary aims to provide a reader with an idea about the settings of a story (such as characters, time and place) without revealing the plot. The approach presented here relies heavily on the notion of aspect. Preliminary results show an improvement over two naive baselines: a lead baseline and a more sophisticated variant of it. Although modest, the results suggest that using aspectual information may be of help when summarizing fiction. A more thorough evaluation involving human judges is under way.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Recursively Summarizing Books with Human Feedback (2021, methodology): “…been work on generating partial summaries of ﬁctional stories: Zhang et al. (2019b) investigate generating character descriptions written by the story author, and Kazantseva (2006) investigate extractive methods for generating information about the story setting and characters, but not the plot.”
BookSum: A Collection of Datasets for Long-form Narrative Summarization (2021, background): “In Kazantseva (2006), the authors focused on generating extractive overviews of short works of ﬁction.”