Indian Language Summarization using Pretrained Sequence-to-Sequence Models

• URL: http://arxiv.org/abs/2303.14461v1
• Date: Sat, 25 Mar 2023 13:05:54 GMT
• Title: Indian Language Summarization using Pretrained Sequence-to-Sequence Models
• Authors: Ashok Urlana, Sahil Manoj Bhatt, Nirmal Surange, Manish Shrivastava
• Abstract summary: The ILSUM task focuses on text summarization for two major Indian languages- Hindi and Gujarati, along with English. We present a detailed overview of the models and our approaches in this paper.
• Score: 11.695648989161878
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The ILSUM shared task focuses on text summarization for two major Indian languages- Hindi and Gujarati, along with English. In this task, we experiment with various pretrained sequence-to-sequence models to find out the best model for each of the languages. We present a detailed overview of the models and our approaches in this paper. We secure the first rank across all three sub-tasks (English, Hindi and Gujarati). This paper also extensively analyzes the impact of k-fold cross-validation while experimenting with limited data size, and we also perform various experiments with a combination of the original and a filtered version of the data to determine the efficacy of the pretrained models.

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