Story Fragment Stitching: The Case of the Story of Moses

Ehsaneddin Asgari, Mark Finlayson

Abstract:

We introduce the task of story fragment stitching,
which is the process of automatically aligning and
merging event sequences of partial tellings of a
story (i.e., story fragments). We assume that each
fragment contains at least one event from the story
of interest, and that every fragment shares at least
one event with another fragment. We propose a
graph-based unsupervised approach to solving this
problem in which events mentions are represented
as nodes in the graph, and the graph is compressed
using a variant of model merging to combine nodes.
The goal is for each node in the final graph to contain only coreferent event mentions. To find coreferent events, we use BERT contextualized embedding in conjunction with a tf-idf vector representation. Constraints on the merge compression preserve the overall timeline of the story, and the final
graph represents the full story timeline. We evaluate our approach using a new annotated corpus of
the partial tellings of the story of Moses found in
the Quran, which we release for public use. Our
approach achieves a performance of 0.63 F1 score