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.
@article{arxiv.2303.14461,
title = {Indian Language Summarization using Pretrained Sequence-to-Sequence Models},
author = {Ashok Urlana and Sahil Manoj Bhatt and Nirmal Surange and Manish Shrivastava},
journal= {arXiv preprint arXiv:2303.14461},
year = {2023}
}
Comments
Accepted at FIRE-2022, Indian Language Summarization (ILSUM) track