Naveen Saini

TL;DR: This paper attempts to build some extractive single document text summarization systems using multi-objective optimization (MOO) frameworks and is able to automatically detect the number of sentence clusters present in a document and then representative sentences are selected from different clusters using some sentence scoring features to generate the summary.

TL;DR: The effectiveness of the proposed approach, namely self-organizing map based multi-objective document clustering technique (SMODoc_clust) is shown in automatic classification of some scientific articles and web-documents.

TL;DR: This study formulated the problem of extractive text-summarization as a binary optimization problem, and multi-objective binary differential evolution (DE) based optimization strategy is employed to solve this, and results illustrate the superiority of the approach in terms of convergence rate and ROUGE scores as compared to state-of-the-art methods.

TL;DR: This article employs the concept of multiobjective optimization in microblog summarization to produce good quality summaries and extension of the proposed approach to solve the multidocument summarization task is illustrated.

TL;DR: A survey of recently developed MOO-based algorithms for solving real-world problems, including bioinformatics, wireless networks, natural language processing, image processing, astronomy and astrophysics, and many more.