Abstractive Text Summarization of Hindi Corpus Using Transformer Encoder-Decoder Model

Text Summarization based on Abstraction is the task of generating a concise summary that captures the principal ideas of the source text. It potentially contains new phrases that do not appear in the original text. Although it is widely studied for languages like English and French, owing to the scarcity of data on regional vernacular languages like Hindi, the research in this area is still in the primitive stages. We propose a novel approach for building an Abstractive Text Summarizer for Hindi corpus using the Transformer encoder-decoder architecture. Firstly, efficient pre-trained word representations are generated using Facebook’s fastText model. Next, the Transformer model is employed to extract contextual dependencies and yield better semantic representations for a morphologically rich language like Hindi, engendering an abstractive summary. On performing an experimental evaluation on the Hindi news dataset to generate news article headlines, we achieve a ROUGE-1 precision and recall score of 0.682 and 0.598, respectively, which outperforms the state-of-the-art techniques.