Automatic text summarization of konkani texts using pre-trained word embeddings and deep learning

Abstract

Automatic text summarization has gained immense popularity in research. Previously, several methods have been explored for obtaining effective text summarization outcomes. However, most of the work pertains to the most popular languages spoken in the world. Through this paper, we explore the area of extractive automatic text summarization using deep learning approach and apply it to Konkani language, which is a low-resource language as there are limited resources, such as data, tools, speakers and/or experts in Konkani. In the proposed technique, Facebook’s fastText
pre-trained word embeddings are used to get a vector representation for sentences. Thereafter, deep multi-layer perceptron technique is employed, as a supervised binary classification task for auto-generating summaries using the feature vectors. Using pre-trained fastText word embeddings eliminated the requirement of a large training set and reduced training time. The system generated summaries were evaluated against the ‘gold-standard’ human generated summaries with recall-oriented understudy for gisting evaluation (ROUGE) toolkit. The results thus obtained showed that performance of the proposed system matched closely to the performance of the human annotators in generating summaries.