Islam, NazmulRahman, SamirSiddique, DaniaTasnim, Humaira SadiaKhan, Zahidul IslamOmar, Nayem Bin2026-08-162026-08-1620262026-01ID 24341192ID 24241088ID 24241055ID 20301158ID 20301435https://hdl.handle.net/10361/29147This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2026.Cataloged from PDF version of thesis.Includes bibliographical references (pages 57-59).Conventional Natural Language Processing (NLP) systems are predominantly designed and trained for monolingual text. However, the extensive use of Bangla-English and Banglish( Bengali written in Romanized alphabets) code-switching informal conversations in digital communication proves to be challenging for these NLP systems. To addresses this research gap in processing mixed language texts of Bangla-English-Banglish we proposed BiLoRA-BN, a end-to-end pipeline specifically designed for normalization and summarization of code-switched Bangla-English-Banglish conversations in digital communication. The proposed system employs a two-stage Low-Rank Adaptation (LoRA) architecture built on a shared, pre-trained Transformer as backbone with 4-bit quantization, enabling efficient multi-task learning while reducing trainable parameters. The experimental results of BiLoRA-BN are compelling, it significantly outperforms conventional sequential and cascading pipelines, with a +5.74 BLEU gain in normalization quality and a +5.26 ROUGE-1 improvement in final summary accuracy. During interface testing BiLoRA-BN also delivers results faster compared to other pipeline based cascading approach of different architecture and pre-trained models. Crucially, in the interface part, the entire system of BiLoRA-BN can operates on a consumer-grade GPUs with 8GB of memory. By directly modeling all the transformations BiLoRA-BN tries to capture the reality of multilingual digital discourse, with the complex scenario like code-switching and code-mixing in the conversations. This work contributes a step toward understanding how people naturally speak and write to communicate in digital spaces and how NLP model work with it.69 pagesen-USAttribution-NonCommercial-NoDerivatives 4.0 InternationalBRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.http://creativecommons.org/licenses/by-nc-nd/4.0/Natural language processingCode-switchingText normalizationAbstractive summarizationLow-rank adaptationLoRAMultilingual modelsQuantizationComputational linguisticsArtificial intelligenceNatural language processing (Computer science).Code switching (Linguistics).Text processing (Computer science).Automatic abstracting.Machine translating.Translating and interpreting.Bengali language--Translating.Thesis