Authors: Pranjali Deshpande, Sunita Jahirabadkar Abstract: Text summarization is an important application of Natural Language Processing (NLP). A huge amount of data is generated everyday through the internet, newspapers, etc. Quick understanding of the documents help reader to save time, retain interest in the reading and provides the clarity of the content. Text summarization facilitates this by two approaches - Extractive and Abstractive. Where extractive approach retains the key phrases and key sentences in the document, abstractive approach focuses on generation of new summary sentences by understanding the crux of document. Summary generation becomes more challenging in case of low resource language documents, as low resource documents lack the large corpora. This paper intends to analyze and compare the techniques used for the abstractive summarization of low resource languages.
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