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

TL;DR

GAE-ISumm is an unsupervised graph autoencoder-based model for summarizing Indian languages, effectively handling low-resource challenges and outperforming existing methods across multiple datasets.

Contribution

The paper introduces GAE-ISumm, a novel unsupervised graph autoencoder model for Indian language summarization, along with a new Telugu dataset TELSUM.

Findings

GAE-ISumm outperforms state-of-the-art models on multiple datasets.

It achieves benchmark results on the TELSUM dataset.

Inclusion of positional and cluster information improves summarization performance.

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.