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.

该研究提出了 Mukhyansh 数据集，其中包含超过 3.39 百万个印度语言标题 - 文章对，用于解决印度语言标题生成中缺乏高质量标注数据的问题，并通过实证分析证明其表现优于其他模型，跨 8 种印度语言平均 ROUGE-L 得分为 31.43。