# Neural Abstractive Text Summarizer for Telugu Language

arXiv: https://arxiv.org/abs/2101.07120 · HTML: https://mlsanity.com/paper/2101.07120/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 4 references that are in the map.
- First posted on arXiv 18 Jan 2021.

## Builds on (most cited first, 4 of 4)

- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [A Neural Attention Model for Abstractive Sentence Summarization](https://mlsanity.com/paper/1509.00685/) (2015)
- [Learning Word Vectors for 157 Languages](https://mlsanity.com/paper/1802.06893/) (2018)
- [Generating News Headlines with Recurrent Neural Networks](https://mlsanity.com/paper/1512.01712/) (2015)

## Built on by (most cited first, 1 of 1)

- [An Overview of Indian Language Datasets used for Text Summarization](https://mlsanity.com/paper/2203.16127/) (2022)

## Abstract (arXiv)

Abstractive Text Summarization is the process of constructing semantically relevant shorter sentences which captures the essence of the overall meaning of the source text. It is actually difficult and very time consuming for humans to summarize manually large documents of text. Much of work in abstractive text summarization is being done in English and almost no significant work has been reported in Telugu abstractive text summarization. So, we would like to propose an abstractive text summarization approach for Telugu language using Deep learning. In this paper we are proposing an abstractive text summarization Deep learning model for Telugu language. The proposed architecture is based on encoder-decoder sequential models with attention mechanism. We have applied this model on manually created dataset to generate a one sentence summary of the source text and have got good results measured qualitatively.