In this work, we introduce L3Cube-IndicNews, a multilingual text
classification corpus aimed at curating a high-quality dataset for Indian
regional languages, with a specific focus on news headlines and articles. We
have centered our work on 10 prominent Indic languages, including Hindi,
Bengali, Marathi, Telugu, Tamil, Gujarati, Kannada, Odia, Malayalam, and
Punjabi. Each of these news datasets comprises 10 or more classes of news
articles. L3Cube-IndicNews offers 3 distinct datasets tailored to handle
different document lengths that are classified as: Short Headlines
Classification (SHC) dataset containing the news headline and news category,
Long Document Classification (LDC) dataset containing the whole news article
and the news category, and Long Paragraph Classification (LPC) containing
sub-articles of the news and the news category. We maintain consistent labeling
across all 3 datasets for in-depth length-based analysis. We evaluate each of
these Indic language datasets using 4 different models including monolingual
BERT, multilingual Indic Sentence BERT (IndicSBERT), and IndicBERT. This
research contributes significantly to expanding the pool of available text
classification datasets and also makes it possible to develop topic
classification models for Indian regional languages. This also serves as an
excellent resource for cross-lingual analysis owing to the high overlap of
labels among languages. The datasets and models are shared publicly at
this https URL

本研究介绍了 L3Cube-IndicNews，一个以新闻标题和文章为重点的多语种文本分类语料库，针对印度区域语言提供高质量的数据集。我们的工作集中在 10 种重要的 Indic 语言上，包括印地语、孟加拉语、马拉地语、泰卢固语、泰米尔语、古吉拉特语、卡纳达语、奥迪亚语、马拉雅拉姆语和旁遮普语。每个新闻数据集都包括 10 个或更多的新闻类别。L3Cube-IndicNews 提供了 3 个不同的数据集，针对不同的文档长度进行分类，分别是：包含新闻标题和新闻类别的 Short Headlines Classification (SHC) 数据集，包含整个新闻文章和新闻类别的 Long Document Classification (LDC) 数据集，以及包含新闻子文章和新闻类别的 Long Paragraph Classification (LPC) 数据集。我们在所有 3 个数据集上采用一致的标签，进行了深度基于长度的分析。我们使用 4 种不同的模型，包括单语 BERT、多语言 Indic 句子 BERT (IndicSBERT) 和 IndicBERT，对每个 Indic 语言数据集进行了评估。本研究在扩大可用的文本分类数据集方面做出了重要贡献，也为开发面向印度区域语言的主题分类模型提供了可能。由于各语言之间标签的高度重叠，这也是进行跨语言分析的极好资源。数据集和模型已公开共享于此 https URL。