MythoBiLLM: BiLSTM-Guided Parameter-Efficient Fine-Tuning of Large Language Models for Coherent Summarization and Generation of Indian Mythological Texts

VoxSumm: A Multilingual Corpus of Long-Form Spoken News for Joint Summarization and Translation

This work formalizes joint speech summarization and translation (JSumT), the generation of a succinct, faithful target-language summary directly from a long spoken document in a source language, and establishes a foundation for developing and evaluating multilingual systems capable of jointly interpreting, compressing, and translating long-form speech.

Enhancing Readability of Telugu Text Summarization Using Multi-scale Attention and Bio-inspired Optimization

The proposed MLOA-MA-ASeqNet architecture, a Multi-scale Attention and Adaptive Sequence-to-Sequence Network whose hierarchical encoder operates simultaneously at word, phrase and sentence level granularity, achieves the highest average score across fluency, adequacy, coherence and readability.

Synergistic Summarization: An Ensemble of Fine-Tuned and Large-Scale Language Models for Factual and Coherent Scientific Abstracts

The exponential growth of scientific literature has intensified the demand for automated summarization systems capable of producing abstracts that are both linguistically fluent and factually reliable. Existing approaches face a fundamental trade-off: encoder-decoder models such as BART and T5 maintain strong factual grounding but produce rigid, extractive outputs, while decoder-only large language models (LLMs) such as Llama and Gemma generate highly fluent text yet remain susceptible to hallucination. This paper proposes a two-stage Synergistic Hybrid Ensemble framework designed to resolve this dichotomy. In Stage 1, a fine-tuned BART-Large model generates a factually grounded scaffold draft from a structured input representation comprising the document title, key sentences, method highlights, and results summary. In Stage 2, a QLoRA-adapted Llama-3.2-1B model performs coherent rewriting and stylistic polishing by conditioning on both the scaffold draft and the original source document. Experiments conducted on the arXiv Scientific Research Papers Dataset using BERTScore and entailment-based Factual Consistency metrics demonstrate that the proposed ensemble achieves a Factual Consistency metrics demonstrate that the proposed ensemble achieves a Factual Consistency score of 0.9140, substantially outperforming BART-Large (0.2890) and Llama-3.2-1B (0.6630) individually. Although the ensemble incurs a marginal reduction in BERTScore (0.8980) relative to Llama-3.2-1B (0.9555), this trade-off is justified given the critical importance of factual reliability in high-stakes scientific discourse. These findings confirm that anchoring the generative capacity of decoder-only LLMs to verified factual scaffolds effectively mitigates hallucination risk, offering a scalable and reproducible solution for high-fidelity scientific abstract generation.

MameLoshnLM: Yiddish Language Model and Evaluation Benchmark

MameLoshnLM, the first open-source 8B-parameter language model built specifically for Yiddish, is presented, providing both a foundation for Yiddish NLP and a practical template for language model development in historically rich but digitally underrepresented languages.

NPRIP: Nucleus-to-Periphery Retrieval-Iterative Prompting for Improved Abstractive Summarization in Low-Resource Mongolian

NERBench-Chhattisgarh: A Multi-Family NER Dataset for Low-Resource Indic Languages

A morphological barrier is shown: while pivot-based adaptation facilitates transfer for distant languages through script alignment, it induces negative transfer in morphologically complex agglutinative languages like Gondi.