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

Abstract

Telugu ranks among the most widely spoken language in South Asia, yet it remains conspicuously underrepresented in automatic summarization research. This neglect is not arbitrary; it reflects three genuine problems viz., Telugu’s agglutinative nature, syntactic representation, and lack of annotated corpora. To address these problems within a unified framework, this paper introduces MLOA-MA-ASeqNet architecture. The architectural core is MA-ASeqNet, a Multi-scale Attention and Adaptive Sequence-to-Sequence Network whose hierarchical encoder operates simultaneously at word, phrase and sentence level granularity. The optimization component, MLOA, is a Modified Lyrebird Optimization Algorithm that replaces manually configured, English-centric hyperparameter defaults with a principled population-based search. Experiments across three datasets – Telugu News NLP, Telugu Books, and TeSum; show consistent and statistically improvements over Seq2Seq, Transformer, T5 and Gemma baselines on ROUGE scores (ROUGE-L of 0.53). Qualitative analysis of the generated summaries was performed through a blind evaluation by five native Telugu speakers from diverse professional backgrounds. The summaries were evaluated on four parameters: fluency, adequacy, coherence, and readability. The proposed MLOA-MA-ASeqNet achieved the highest average score of 4.60 across fluency, adequacy, coherence and readability; surpassing all four baselines on every dimension. All pairwise differences were statistically significant according to the Wilcoxon signed-rank test with Bonferroni correction (p < 0.05 in all cases).