Nikhilesh Bhatnagar


2023

pdf bib
Automatic Data Retrieval for Cross Lingual Summarization
Nikhilesh Bhatnagar | Ashok Urlana | Pruthwik Mishra | Vandan Mujadia | Dipti M. Sharma
Proceedings of the 20th International Conference on Natural Language Processing (ICON)

Cross-lingual summarization involves the sum marization of text written in one language to a different one. There is a body of research addressing cross-lingual summarization from English to other European languages. In this work, we aim to perform cross-lingual summarization from English to Hindi. We propose pairing up the coverage of newsworthy events in textual and video format can prove to be helpful for data acquisition for cross lingual summarization. We analyze the data and propose methods to match articles to video descriptions that serve as document and summary pairs. We also outline filtering methods over reasonable thresholds to ensure the correctness of the summaries. Further, we make available 28,583 mono and cross-lingual article-summary pairs* . We also build and analyze multiple baselines on the collected data and report error analysis.

2018

pdf bib
Exploring Chunk Based Templates for Generating a subset of English Text
Nikhilesh Bhatnagar | Manish Shrivastava | Radhika Mamidi
Proceedings of ACL 2018, Student Research Workshop

Natural Language Generation (NLG) is a research task which addresses the automatic generation of natural language text representative of an input non-linguistic collection of knowledge. In this paper, we address the task of the generation of grammatical sentences in an isolated context given a partial bag-of-words which the generated sentence must contain. We view the task as a search problem (a problem of choice) involving combinations of smaller chunk based templates extracted from a training corpus to construct a complete sentence. To achieve that, we propose a fitness function which we use in conjunction with an evolutionary algorithm as the search procedure to arrive at a potentially grammatical sentence (modeled by the fitness score) which satisfies the input constraints.