# Mukhyansh: A Headline Generation Dataset for Indic Languages

arXiv: https://arxiv.org/abs/2311.17743 · HTML: https://mlsanity.com/paper/2311.17743/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 20 references that are in the map.
- First posted on arXiv 29 Nov 2023.

## Builds on (most cited first, 12 of 12)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Multilingual Denoising Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2001.08210/) (2020)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages](https://mlsanity.com/paper/2106.13822/) (2021)
- [Learning Word Vectors for 157 Languages](https://mlsanity.com/paper/1802.06893/) (2018)
- [BPEmb: Tokenization-free Pre-trained Subword Embeddings in 275 Languages](https://mlsanity.com/paper/1710.02187/) (2017)
- [IndicNLG Benchmark: Multilingual Datasets for Diverse NLG Tasks in Indic Languages](https://mlsanity.com/paper/2203.05437/) (2022)
- [IndicBART: A Pre-trained Model for Indic Natural Language Generation](https://mlsanity.com/paper/2109.02903/) (2021)

## Built on by (most cited first, 5 of 5)

- [IndicGenBench: A Multilingual Benchmark to Evaluate Generation Capabilities of LLMs on Indic Languages](https://mlsanity.com/paper/2404.16816/) (2024)
- [SemRel2024: A Collection of Semantic Textual Relatedness Datasets for 13 Languages](https://mlsanity.com/paper/2402.08638/) (2024)
- [IndicParam: Benchmark to evaluate LLMs on low-resource Indic Languages](https://mlsanity.com/paper/2512.00333/) (2025)
- [BeliN: A Novel Corpus for Bengali Religious News Headline Generation using Contextual Feature Fusion](https://mlsanity.com/paper/2501.01069/) (2025)
- [TeClass: A Human-Annotated Relevance-based Headline Classification and Generation Dataset for Telugu](https://mlsanity.com/paper/2404.11349/) (2024)

## Abstract (arXiv)

The task of headline generation within the realm of Natural Language Processing (NLP) holds immense significance, as it strives to distill the true essence of textual content into concise and attention-grabbing summaries. While noteworthy progress has been made in headline generation for widely spoken languages like English, there persist numerous challenges when it comes to generating headlines in low-resource languages, such as the rich and diverse Indian languages. A prominent obstacle that specifically hinders headline generation in Indian languages is the scarcity of high-quality annotated data. To address this crucial gap, we proudly present Mukhyansh, an extensive multilingual dataset, tailored for Indian language headline generation. Comprising an impressive collection of over 3.39 million article-headline pairs, Mukhyansh spans across eight prominent Indian languages, namely Telugu, Tamil, Kannada, Malayalam, Hindi, Bengali, Marathi, and Gujarati. We present a comprehensive evaluation of several state-of-the-art baseline models. Additionally, through an empirical analysis of existing works, we demonstrate that Mukhyansh outperforms all other models, achieving an impressive average ROUGE-L score of 31.43 across all 8 languages.