Explorations in Graph-Based Ranking Algorithms for Automatic Text Summarization on Konkani Texts

Abstract

AbstractThe work presented in this paper is an attempt at exploring the field of automatic text summarization and applying it to Konkani language, which is one of the low-resource languages in the automatic text summarization domain. Low-resource languages are the ones that have none or a very limited number of existing resources available, such as data, tools, language experts, and so on. We examine popular graph-based ranking algorithms and evaluate their performance in performing unsupervised automatic text summarization. The text is represented as a graph, where the vertices represent sentences and the edges between a pair of vertices represent a similarity score computed using a similarity measure. The graph-based ranking algorithms then rank the most relevant vertices (or sentences) to include in a summary. This paper also examines the impact of using weighted undirected or directed graphs on the output of the summarization system. The dataset used in the experiments was specially constructed by the authors using books on Konkani literature, and it is written in Devanagari script. The results of the experiments indicate that the graph-based ranking algorithms produce promising summaries of arbitrary length without needing any resources or training data. These algorithms can be effortlessly extended to other low-resource languages to get favorable results.KeywordsAutomatic text summarizationKonkani text summarizationTextRankGraph-based text summarizationLow-resource languages