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Abstract

Automatic text alignment is an important problem in natural language processing. It
can be used to create the data needed to train different language models. Most research
about automatic summarization revolves around summarizing news articles or scientific
papers, which are somewhat small texts with simple and clear structure. The bigger the
difference in size between the summary and the original text, the harder the problem will
be since important information will be sparser and identifying them can be more difficult.
Therefore, creating datasets from larger texts can help improve automatic summarization.
In this project, we try to develop an algorithm which can automatically create a
dataset for abstractive automatic summarization for bigger narrative text bodies such
as movie scripts. To this end, we chose sentences as summary text units and scenes
as script text units and developed an algorithm which uses some of the latest natural
language processing techniques to align scenes and sentences based on the similarity in
their meanings.
Solving this alignment problem can provide us with important information about how
to evaluate the meaning of a text, which can help us create better abstractive summariza-
tion models. We developed a method which uses different similarity scoring techniques
(embedding similarity, word inclusion and entity inclusion) to align script scenes and sum-
mary sentences which achieved an F1 score of 0.39. Analyzing our results showed that
the bigger the differences in the number of text units being aligned, the more difficult the
alignment problem is. We also critiqued of our own similarity scoring techniques and dif-
ferent alignment algorithms based on integer linear programming and local optimization
and showed their limitations and discussed ideas to improve them.