An extractive text summarization approach using tagged-LDA based topic modeling

Abstract

Automatic text summarization is an exertion of contriving the abridged form of a text document covering salient knowledge. Numerous statistical, linguistic, rule-based, and position-based text summarization approaches have been explored for different rich-resourced languages. For under-resourced languages such as Hindi, automatic text summarization is a challenging task and still an unsolved problem. Another issue with such languages is the unavailability of corpus and the inadequacy of the processing tools. In this paper, we proposed an extractive lexical knowledge-rich topic modeling text summarization approach for Hindi novels and stories in which we implemented four independent variants using different sentence weighting schemes. We prepared a corpus of Hindi Novels and stories since the absence of a corpus. We used a smoothing technique for edifying and variety summaries followed by evaluating the efficacy of generated summaries against three metrics (gist diversity, retention ratio, and ROUGE score). The results manifest that the proposed model produces abridge, articulate and coherent summaries. To investigate the performance of the proposed model, we simulate the experiments on the English dataset as well. Further, we compare our models with the baselines and traditional topic modeling approach, where we show that the proposed model has confessed optimal results.