Unsupervised Automatic Text Summarization of Konkani Texts using K-means with Elbow Method

Abstract

Text Summarization is an emerging field of research in Natural Language Processing (NLP). A bulk of the work is related to texts in English and other popular languages. This paper presents some of the early works attempted at performing single document extractive Automatic Text Summarization on Konkani language documents, which is an under-research language in the domain of Automatic Text Summarization (ATS). The input documents need to be cleaned of punctuation and then sentence scores are calculated for each sentence in the document. The scores for each sentence are computed using Term-Frequency/Inverse Document Frequency (TF-IDF) of constituent words and overlap with the title of the story and its positional value. K-means algorithm is applied to determine clusters of sentences for the formation of the final summary. The value of ‘K’ is determined using the Elbow method. The dataset employed was specially designed by the authors of the paper to perform the experiments. It consists of folk tales derived from books on Konkani literature. The performance assessment of the output summaries indicated that the summaries obtained by using three clusters were better than the ones obtained using two clusters. The proposed system exhibited promising outcome, considering, no language-dependent domain knowledge or any training corpora was utilized.