Can Classical Semantic-Extractive Summarization Be Evaluated in Hindi? A Replication Study

Title:Can Classical Semantic-Extractive Summarization Be Evaluated in Hindi? A Replication Study

Abstract:We replicate the distributional-semantics extractive summarisation method of Mohd, Jan and Shah (2020) and adapt it to Hindi, substituting a Devanagari-appropriate component at every language-specific step. The system is evaluated on two independent corpora --- the Hindi portion of XL-Sum and FIRE ILSUM 2.0 Hindi --- under a Devanagari-aware ROUGE implementation validated against the XL-Sum authors' own multilingual scorer, with all comparisons drawn as 1000-resample paired bootstraps. In its published equal-weight configuration the replicated system is significantly worse than a three-sentence lead baseline on both corpora, trailing Lead-3 by 0.042 ROUGE-1 Fon XL-Sum and by 0.265 on ILSUM. A feature ablation shows that sentenceposition is the only feature that contributes: position alone reproduces the lead baseline exactly, removing position gives the weakest configuration,and a validation-tuned weighting can at best equal Lead-3 and never exceed it. TextRank fails identically, making this a class-level rather than an implementation-level result. A selection analysis shows the remaining features steer extraction towards long, entity-dense body sentences while the references reuse the article this http URL Hindi benchmarks therefore cannot reward non-lead content selection, motivating purpose-built evaluation resources.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2609.29090 [cs.CL]

(or arXiv:2609.29090v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.29090 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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