Scaled Document Clustering and Word Cloud-Based Summarization on Hindi Corpus

Managing a large number of textual documents is a critical and significant task and supports many applications ranging from information retrieval to clustering search engine results. The multilinguistic facility provided by websites makes Hindi a major language in the digital domain of information technology today. This work focuses on document management through document clustering for a big corpus and summarization of clusters. The objective is to overcome the scalability problem while managing the documents and summarizing the Hindi corpus by extracting tokens. The work is better in terms of scalability and supports the consistent quality of cluster for incremental dataset. Most of the past and contemporary research works have targeted English corpus document management. Hindi corpus has been mostly exploited by the researchers for exploring stemming, single-document summarization, and classifier design. Implementing unsupervised learning on the Hindi corpus for summarization of multiple documents through Word Cloud is still an untouched area. Technically speaking, the current work is an application of TF-IDF, cosine-based document similarity measures, and cluster dendrograms, in addition to various other Natural Language Processing (NLP) activities. Entropy and precision are used to evaluate the experiments carried on different live and available/tested datasets and results prove the robustness of the proposed approach for Hindi Corpus.