GAE-ISumm: Unsupervised Graph-Based Summarization of Indian Languages

• URL: http://arxiv.org/abs/2212.12937v1
• Date: Sun, 25 Dec 2022 17:20:03 GMT
• Title: GAE-ISumm: Unsupervised Graph-Based Summarization of Indian Languages
• Authors: Lakshmi Sireesha Vakada, Anudeep Ch, Mounika Marreddy, Subba Reddy Oota, Radhika Mamidi
• Abstract summary: Document summarization aims to create a precise and coherent summary of a text document. Many deep learning summarization models are developed mainly for English, often requiring a large training corpus. We propose GAE-ISumm, an unsupervised Indic summarization model that extracts summaries from text documents.
• Score: 5.197307534263253
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Document summarization aims to create a precise and coherent summary of a text document. Many deep learning summarization models are developed mainly for English, often requiring a large training corpus and efficient pre-trained language models and tools. However, English summarization models for low-resource Indian languages are often limited by rich morphological variation, syntax, and semantic differences. In this paper, we propose GAE-ISumm, an unsupervised Indic summarization model that extracts summaries from text documents. In particular, our proposed model, GAE-ISumm uses Graph Autoencoder (GAE) to learn text representations and a document summary jointly. We also provide a manually-annotated Telugu summarization dataset TELSUM, to experiment with our model GAE-ISumm. Further, we experiment with the most publicly available Indian language summarization datasets to investigate the effectiveness of GAE-ISumm on other Indian languages. Our experiments of GAE-ISumm in seven languages make the following observations: (i) it is competitive or better than state-of-the-art results on all datasets, (ii) it reports benchmark results on TELSUM, and (iii) the inclusion of positional and cluster information in the proposed model improved the performance of summaries.

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