# Automated Story Generation as Question-Answering

Source: [Automated Story Generation as Question-Answering](<https://www.sota2.com/research/paper/68fa6b5e1e209131cea2dc5d>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:47:07.195Z

Authors: Louis Castricato, Spencer Frazier, Jonathan Balloch, Nitya Tarakad, Mark Riedl

Published: 2021-12-07T16:32:30.000Z

## Abstract

Neural language model-based approaches to automated story generation suffer from two important limitations. First, language model-based story generators generally do not work toward a given goal or ending. Second, they often lose coherence as the story gets longer. We propose a novel approach to automated story generation that treats the problem as one of generative question-answering. Our proposed story generation system starts with sentences encapsulating the final event of the story. The system then iteratively (1) analyzes the text describing the most recent event, (2) generates a question about "why" a character is doing the thing they are doing in the event, and then (3) attempts to generate another, preceding event that answers this question.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2112.03808>)

[PDF](<https://arxiv.org/pdf/2112.03808.pdf>)