Related papers: TVRecap: A Dataset for Generating Stories with Character Descriptions

TVRecap: A Dataset for Generating Stories with Character Descriptions

URL: http://arxiv.org/abs/2109.08833v1
Date: Sat, 18 Sep 2021 05:02:29 GMT
Title: TVRecap: A Dataset for Generating Stories with Character Descriptions
Authors: Mingda Chen, Kevin Gimpel
Abstract summary: TVRecap is a story generation dataset that generates detailed TV show episode recaps from a brief summary and documents describing the characters involved. We create TVRecap from fan-contributed websites, which allows us to collect 26k episode recaps with 1868.7 tokens on average.
Score: 43.198875830024825
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
Abstract: We introduce TVRecap, a story generation dataset that requires generating detailed TV show episode recaps from a brief summary and a set of documents describing the characters involved. Unlike other story generation datasets, TVRecap contains stories that are authored by professional screenwriters and that feature complex interactions among multiple characters. Generating stories in TVRecap requires drawing relevant information from the lengthy provided documents about characters based on the brief summary. In addition, by swapping the input and output, TVRecap can serve as a challenging testbed for abstractive summarization. We create TVRecap from fan-contributed websites, which allows us to collect 26k episode recaps with 1868.7 tokens on average. Empirically, we take a hierarchical story generation approach and find that the neural model that uses oracle content selectors for character descriptions demonstrates the best performance on automatic metrics, showing the potential of our dataset to inspire future research on story generation with constraints. Qualitative analysis shows that the best-performing model sometimes generates content that is unfaithful to the short summaries, suggesting promising directions for future work.

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