Abstract
Structured story graphs have proven to be useful for representing content in pipelines for automated interpretation and narration. Recent progress on interpretation using logical abduction has made it possible to construct these representations automatically, and several methods for converting these structures into narrative text have been proposed. In this paper, we describe a technical approach to narrative text generation from structured story graphs that prioritizes simplicity and ease-of-use, employing full-sentence templates associated with the specific axioms used to construct graphs during the interpretation process. We evaluate our approach using the TriangleCOPA benchmark for narrative interpretation and text generation, comparing our results to human-authored narratives and to the results of previous work.
The project or effort depicted was or is sponsored by the U.S. Army Research Laboratory (ARL) under contract number W911NF-14-D-0005, and that the content of the information does not necessarily reflect the position or the policy of the Government, and no official endorsement should be inferred.
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Gordon, A.S., Wang, T.S. (2021). Narrative Text Generation from Abductive Interpretations Using Axiom-Specific Templates. In: Mitchell, A., Vosmeer, M. (eds) Interactive Storytelling. ICIDS 2021. Lecture Notes in Computer Science(), vol 13138. Springer, Cham. https://doi.org/10.1007/978-3-030-92300-6_7
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DOI: https://doi.org/10.1007/978-3-030-92300-6_7
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