Enhancing Readability of Telugu Text Summarization Using Multi-scale Attention and Bio-inspired Optimization
TL;DR
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.
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).