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Cross Lingual Abstractive Summarization for Marathi
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arXiv AI
6d ago
Cross Lingual Abstractive Summarization for Marathi
The paper presents a cross‑lingual abstractive summarization system for Marathi, leveraging the Marathi MahaSum corpus and the smaller PMIndiaSum dataset to fine‑tune the IndicTrans2 neural machine translation model. Evaluation on Marathi‑to‑English summarization using ROUGE, chrF, and chrF++ metrics shows that this approach outperforms prior IndicBART and mBART‑50 baselines. The authors also discuss extending the method to English‑to‑Marathi summarization.
arXiv Machine Learning
Jun 24
L3Cube-MahaPOS: A Marathi Part-of-Speech Tagging Dataset and BERT Models
arXiv:2606. 24825v1 Announce Type: cross Abstract: Part-of-Speech (POS) tagging is a foundational NLP task underpinning machine translation, information extraction, and syntactic parsing.
arXiv Computation and Language
Sep 25
COILD: An Indic-Centric Parallel Corpus and Benchmark for Machine Translation Across Indian Languages
COILD is an Indic‑centric parallel corpus that contains over 1.16 million human‑translated and verified sentence pairs across 20 Indian language pairs from four language families. The corpus is sourced from original Indian language materials in eight domains, and a 2,000‑sentence domain‑centric benchmark is provided for consistent multilingual evaluation. Experiments with IndicTrans2‑Distilled and NLLB‑200 show consistent improvements in automatic metrics and human judgments, underscoring the value of high‑quality Indic‑centric data.
arXiv AI
Jun 9
AgriGov: A Structured Multilingual Dataset Curation for Indian Government Schemes for Farmers
arXiv:2606. 08272v1 Announce Type: cross Abstract: AgriGov is a curated, trilingual (English-Hindi-Marathi) dataset designed to address the scarcity of domain-grounded multilingual resources for agricultural policies and farmer welfare schemes.
arXiv AI
Sep 25
Can Classical Semantic-Extractive Summarization Be Evaluated in Hindi? A Replication Study
The study replicates a distributional‑semantics extractive summarisation method for Hindi, adapting all language‑specific components to Devanagari. Evaluated on the Hindi portions of XL‑Sum and FIRE ILSUM 2.0 with a Devanagari‑aware ROUGE scorer, the replicated system performs significantly worse than a simple three‑sentence lead baseline. Feature ablation shows that sentence position alone reproduces the lead baseline, while other features only steer extraction toward long, entity‑dense body sentences, and TextRank performs identically. "whyItMatters":"The results indicate that current Hindi summarisation benchmarks cannot reward non‑lead content selection, highlighting the need for purpose‑built evaluation resources."
arXiv Computation and Language
Oct 15
Evaluating Large Language Models for Multilingual Abstractive Summarization: A Comprehensive Study of English and Bengali
The study introduces a zero‑shot benchmark for eight large language models on English and Bengali abstractive summarization, using both lexical/semantic metrics and reference‑free factuality checks. Results show significant cross‑lingual differences: GPT‑5.6 Luna excels in English but not in Bengali, where LLaMA 3.3 70B tops ROUGE and BLEU, while Gemini 2.5 Flash achieves the best Bengali factuality score. The evaluation also reveals that different metrics rank models differently, underscoring the need for multi‑dimensional assessment in low‑resource languages.