Chaitali More
ABSTRACT
Automatic text summarization has been widely researched for the most popularly spoken languages. There is a need to extend research to other less popular and low-resource languages as well. We explore automatic text summarization for Konkani language. It is a language spoken by a relatively small population of people in the state of Goa, India, and is a low-resource language. A low-resource language has limited language processing tools, which add to the challenges faced in the automatic text summarization process. This study aims at extending the popular graph-based language-independent approach using language-independent keywords with the help of YAKE. These keywords allow us to weight the edges in a fully connected undirected graph by not only considering the influence of two vertices (sentences) but also the sentences with the overall document, represented by the keywords. Variable thresholds of relevant keywords are examined along with altering a bias parameter. The impact of these keyword thresholds along with the bias parameter is examined on the system generated summary.
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Index Terms
- Keyword Driven Language-Independent Low-Resource Graph-Based Automatic Text Summarization of Konkani Texts
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