text segmentation , graph cliques , natural language processing
Abstract:
Segmenting text into semantically coherent
fragments improves readability of text
and facilitates tasks like text summarization
and passage retrieval. In this paper,
we present a novel unsupervised algorithm
for linear text segmentation (TS)
that exploits word embeddings and a measure
of semantic relatedness of short texts
to construct a semantic relatedness graph
of the document. Semantically coherent
segments are then derived from maximal
cliques of the relatedness graph. The algorithm
performs competitively on a standard
synthetic dataset and outperforms the
best-performing method on a real-world
(i.e., non-artificial) dataset of political manifestos.
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