mlsanityData to 14 Oct 2026

Mukhyansh: A Headline Generation Dataset for Indic Languages

arXiv 2311.17743 ↗First posted 29 Nov 2023cs.CL cs.AIcited by 5

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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.

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

12 of 20 references.

References · 12

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  2. 2014Sequence to Sequence Learning with Neural Networkscited by 2,986
  3. 2020mT5: A massively multilingual pre-trained text-to-text transformercited by 1,818
  4. 2016Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translationcited by 1,475
  5. 2014On the Properties of Neural Machine Translation: Encoder-Decoder Approachescited by 1,304
  6. 2020Multilingual Denoising Pre-training for Neural Machine Translationcited by 1,035
  7. 2015Effective Approaches to Attention-based Neural Machine Translationcited by 1,026
  8. 2021XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languagescited by 347
  9. 2018Learning Word Vectors for 157 Languagescited by 280
  10. 2017BPEmb: Tokenization-free Pre-trained Subword Embeddings in 275 Languagescited by 52
  11. 2022IndicNLG Benchmark: Multilingual Datasets for Diverse NLG Tasks in Indic Languagescited by 42
  12. 2021IndicBART: A Pre-trained Model for Indic Natural Language Generationcited by 39

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Cited by year

Citing papers per year: 2024: 3; 2025: 2.

5 papers.

  1. 2024IndicGenBench: A Multilingual Benchmark to Evaluate Generation Capabilities of LLMs on Indic Languagescited by 56
  2. 2024SemRel2024: A Collection of Semantic Textual Relatedness Datasets for 13 Languagescited by 16
  3. 2025IndicParam: Benchmark to evaluate LLMs on low-resource Indic Languagescited by 3
  4. 2025BeliN: A Novel Corpus for Bengali Religious News Headline Generation using Contextual Feature Fusioncited by 1
  5. 2024TeClass: A Human-Annotated Relevance-based Headline Classification and Generation Dataset for Telugucited by 0