STORIUM: A Dataset and Evaluation Platform for Machine-in-the-Loop Story Generation

TL;DR

This paper introduces STORIUM, a large, richly annotated storytelling dataset and an evaluation platform that enables collaborative, human-in-the-loop assessment of story generation models, addressing current limitations in data quality and evaluation reliability.

Contribution

It provides a novel dataset with detailed annotations and an interactive platform for evaluating story generation models through human collaboration.

Findings

Automatic metrics correlate with user ratings

Models fine-tuned on STORIUM produce more plausible stories

The platform facilitates iterative story refinement

Abstract

Systems for story generation are asked to produce plausible and enjoyable stories given an input context. This task is underspecified, as a vast number of diverse stories can originate from a single input. The large output space makes it difficult to build and evaluate story generation models, as (1) existing datasets lack rich enough contexts to meaningfully guide models, and (2) existing evaluations (both crowdsourced and automatic) are unreliable for assessing long-form creative text. To address these issues, we introduce a dataset and evaluation platform built from STORIUM, an online collaborative storytelling community. Our author-generated dataset contains 6K lengthy stories (125M tokens) with fine-grained natural language annotations (e.g., character goals and attributes) interspersed throughout each narrative, forming a robust source for guiding models. We evaluate language models fine-tuned on our dataset by integrating them onto STORIUM, where real authors can query a model for suggested story continuations and then edit them. Automatic metrics computed over these edits correlate well with both user ratings of generated stories and qualitative feedback from semi-structured user interviews. We release both the STORIUM dataset and evaluation platform to spur more principled research into story generation.