  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.
