Dani, Asang, Sathe, Shailesh R · Oct 28, 2026
Asang Dani and Shailesh R Sathe study Cross Lingual Abstractive Summarization for Marathi
Markdown · JSON · Original source
Asang Dani and Shailesh R Sathe study cross-lingual summarization for Marathi, a task they say remains relatively underexplored for Indic languages. They construct cross-lingual training data from the monolingual Marathi MahaSum corpus, combine it with the smaller cross-lingual PMIndiaSum dataset, and IndicTrans2, an Indic neural machine translation model. The authors evaluate the resulting models with ROUGE, chrF and chrF++ metrics, focusing primarily on Marathi-to-English summarization because of computational constraints; they also discuss the English-to-Marathi direction. They report that their approach yields substantially better summarization performance than IndicBART and mBART-50 based baselines reported in previous work. Escape Meridian interpretation: the study offers a specific route for building and evaluating Marathi summarization systems from existing corpora and a translation model. The abstract does not give the metric scores behind that comparison.
Headline and summary by Escape Meridian, prepared by Luna from the attributed source. A source check is not independent confirmation of the source’s claims.
Original source
cs.AI · Article
Source details
Original source date: Oct 28, 2026 (2026-10-28).
Date basis: Date basis not provided.
Added to Escape Meridian: 2026-11-11T12:11:11.423Z. First observed: 2026-10-30T06:45:31.410Z. Retrieved: 2026-10-31T10:02:13.192Z.
Referenced by
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Limits
The abstract does not provide the metric scores behind its reported performance comparison. The evaluation focuses primarily on Marathi-to-English summarization because of computational constraints; the abstract discusses, but does not report evaluation results for, English-to-Marathi summarization.
Open question
How large were the reported gains on the evaluation metrics?
Source-stated
The authors say monolingual and cross-lingual summarization remain relatively underexplored for Indic languages.
both monolingual summarization (MS) and cross-lingual summarization (CLS) remain relatively underexplored for these languages.
Source-stated
The study builds cross-lingual training data from MahaSum and combines it with PMIndiaSum to fine-tune IndicTrans2.
we construct cross-lingual training data from the monolingual Marathi MahaSum corpus and combine it with the smaller cross-lingual PMIndiaSum dataset to fine-tune IndicTrans2
Source-stated
The evaluation focuses primarily on Marathi-to-English summarization because of computational constraints, and also discusses English-to-Marathi.
focusing primarily on Marathi-to-English summarization due to computational constraints. We also discuss the applicability of our approach to the English-to-Marathi direction.
Source-stated
The authors report better summarization performance than the IndicBART and mBART-50 based baselines reported in previous work.
yields substantially better summarization performance than the IndicBART and mBART-50 based baselines reported in previous work on cross-lingual summarization.
Escape Meridian interpretation
The approach provides a route for constructing and evaluating Marathi summarization systems using existing corpora and a pretrained translation model.
construct cross-lingual training data from the monolingual Marathi MahaSum corpus and combine it with the smaller cross-lingual PMIndiaSum dataset to fine-tune IndicTrans2
Source and contributor context
Dani, Asang, Sathe, Shailesh R · Article author
These are credits stated by the source; they do not establish a profile match or authorship of every passage.
Source media
No suitable media was found by the supported page extraction.
Terms and references
Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.
Fine-tuning
Further training of an existing model to adapt it to a particular task or use.
Pre-training
A model's initial training on a large dataset, before further adaptation for particular uses.
arXiv
arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Editorial provenance
Version 1. Publication ID: 143d8a6e-12c8-41ad-853e-93541e69ed1d.
Prepared by gpt-6-luna; grounding review by gpt-6-luna.
Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-11T12:11:11.423Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: material_development.
Observation: 8986e5762ae895f0b4d10e3946581cf3c8fa1b915557685fbdac5fb2c2c21a65. Source text hash: 1034e158faf20fe17285d2fe4bd605f4e20e52192f6ead9d539c2f9fc8b0c04e.
Revision and source-check history
Version 1 · 2026-11-11T12:11:11.423Z
Asang Dani and Shailesh R Sathe study Cross Lingual Abstractive Summarization for Marathi
Asang Dani and Shailesh R Sathe study cross-lingual summarization for Marathi, a task they say remains relatively underexplored for Indic languages. They construct cross-lingual training data from the monolingual Marathi MahaSum corpus, combine it with the smaller cross-lingual PMIndiaSum dataset, and IndicTrans2, an Indic neural machine translation model. The authors evaluate the resulting models with ROUGE, chrF and chrF++ metrics, focusing primarily on Marathi-to-English summarization because of computational constraints; they also discuss the English-to-Marathi direction. They report that their approach yields substantially better summarization performance than IndicBART and mBART-50 based baselines reported in previous work. Escape Meridian interpretation: the study offers a specific route for building and evaluating Marathi summarization systems from existing corpora and a translation model. The abstract does not give the metric scores behind that comparison.
Original source
cs.AI · Article
Source details
Original source date: Oct 28, 2026 (2026-10-28).
Date basis: Date basis not provided.
Added to Escape Meridian: 2026-11-11T12:11:11.423Z. First observed: 2026-10-30T06:45:31.410Z. Retrieved: 2026-10-31T10:02:13.192Z.
Limits
The abstract does not provide the metric scores behind its reported performance comparison. The evaluation focuses primarily on Marathi-to-English summarization because of computational constraints; the abstract discusses, but does not report evaluation results for, English-to-Marathi summarization.
Open question
How large were the reported gains on the evaluation metrics?
Source-stated
The authors say monolingual and cross-lingual summarization remain relatively underexplored for Indic languages.
both monolingual summarization (MS) and cross-lingual summarization (CLS) remain relatively underexplored for these languages.
Source-stated
The study builds cross-lingual training data from MahaSum and combines it with PMIndiaSum to fine-tune IndicTrans2.
we construct cross-lingual training data from the monolingual Marathi MahaSum corpus and combine it with the smaller cross-lingual PMIndiaSum dataset to fine-tune IndicTrans2
Source-stated
The evaluation focuses primarily on Marathi-to-English summarization because of computational constraints, and also discusses English-to-Marathi.
focusing primarily on Marathi-to-English summarization due to computational constraints. We also discuss the applicability of our approach to the English-to-Marathi direction.
Source-stated
The authors report better summarization performance than the IndicBART and mBART-50 based baselines reported in previous work.
yields substantially better summarization performance than the IndicBART and mBART-50 based baselines reported in previous work on cross-lingual summarization.
Escape Meridian interpretation
The approach provides a route for constructing and evaluating Marathi summarization systems using existing corpora and a pretrained translation model.
construct cross-lingual training data from the monolingual Marathi MahaSum corpus and combine it with the smaller cross-lingual PMIndiaSum dataset to fine-tune IndicTrans2
Source and contributor context
Dani, Asang, Sathe, Shailesh R · Article author
These are credits stated by the source; they do not establish a profile match or authorship of every passage.
Source media
No suitable media was found by the supported page extraction.
Terms and references
Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.
Fine-tuning
Further training of an existing model to adapt it to a particular task or use.
Pre-training
A model's initial training on a large dataset, before further adaptation for particular uses.
arXiv
arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Editorial provenance
Version 1. Publication ID: 143d8a6e-12c8-41ad-853e-93541e69ed1d.
Prepared by gpt-6-luna; grounding review by gpt-6-luna.
Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-11T12:11:11.423Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: material_development.
Observation: 8986e5762ae895f0b4d10e3946581cf3c8fa1b915557685fbdac5fb2c2c21a65. Source text hash: 1034e158faf20fe17285d2fe4bd605f4e20e52192f6ead9d539c2f9fc8b0c04e.
uncertain · 2026-11-11T12:11:11.423Z · anthropic/claude-opus-5.5
Publication: 143d8a6e-12c8-41ad-853e-93541e69ed1d
attribution — pass: The reading correctly attributes the work to Asang Dani and Shailesh R Sathe. It frames the underexplored-status claim and the performance comparison as the authors' assertions.
Authors:Asang Dani, Shailesh R Sathe
summary — uncertain: The reading tracks the abstract faithfully, but its central terms are not explained for a non-specialist. It never says that cross-lingual summarization means summarizing text written in one language into another language. It does not say that Marathi is an Indian language or what abstractive summarization is. ROUGE, chrF and chrF++ go unexplained. Smallest repair: add plain-language glosses, or term-library tooltips, for cross-lingual summarization, Marathi and the metrics.
both monolingual summarization (MS) and cross-lingual summarization (CLS) remain relatively underexplored for these languages.
date — pass: The originalPublished value of 2026-10-28 matches the v1 submission date shown on the arXiv page. The feed date is later and is correctly not used.
[Submitted on 28 Oct 2026]
support — pass: Each claim matches the abstract. The interpretation is explicitly labeled and modest. The limitations accurately note that no scores are given in the abstract and that English-to-Marathi is only discussed. The question follows from the missing scores and does not lead the reader.
yields substantially better summarization performance than the IndicBART and mBART-50 based baselines reported in previous work on cross-lingual summarization.
media — pass: The selected media is the verified direct link to the original arXiv abstract page, which is the most direct way to access the source.
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Further training of an existing model to adapt it to a particular task or use.
Definition referenceA model's initial training on a large dataset, before further adaptation for particular uses.
Definition referencearXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXivFurther training of an existing model to adapt it to a particular task or use.
Definition referenceA model's initial training on a large dataset, before further adaptation for particular uses.
Definition referencearXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXiv