ACL 2021short6 citations
AligNarr: Aligning Narratives on Movies
Paramita Mirza, Mostafa Abouhamra, Gerhard Weikum
Abstract
High-quality alignment between movie scripts and plot summaries is an asset for learning to summarize stories and to generate dialogues. The alignment task is challenging as scripts and summaries substantially differ in details and abstraction levels as well as in linguistic register. This paper addresses the alignment problem by devising a fully unsupervised approach based on a global optimization model. Experimental results on ten movies show the viability of our method with 76% F1-score and its superiority over a previous baseline. We publish alignments for 914 movies to foster research in this new topic.
BibTeX
@inproceedings{mirza-etal-2021-alignarr,
title = "{A}lig{N}arr: Aligning Narratives on Movies",
author = "Mirza, Paramita and
Abouhamra, Mostafa and
Weikum, Gerhard",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-short.54/",
doi = "10.18653/v1/2021.acl-short.54",
pages = "427--433"
}