Jul 2026· 2026 7th International Conference on Smart Systems and Inventive Technology (ICSSIT)· pp. 1694-1700· 0 citations· 20 references
Abstract
India is home to an incredible number of languages which leads to the production of substantial amounts of news articles provided in their regional languages including Telugu, Tamil, Hindi, Bengali, Kannada, and Malayalam. Unfortunately, current systems for processing this data do so independently, rather than as part of a comprehensive framework utilizing all the necessary components in one queue of processing pipelines; OCR extraction, Translation, Summarization, and Bias Detection must all be completed one at a time, and do not allow for seamless data passing between functions. In this paper, we will present Bharat Sum, a multilingual news summarization and bias detection system that incorporates OCR capabilities into five different types of processing stages; OCR Text Extraction using Tesseract, Automatic Language Detection using LangDetect, Topic Segmented Abstractive Summarisation using mT5, Translation to English using mBART, and Sentiment based Bias Classification using DistilBERT - all accessible through a single scalable architecture running on commodity hardware and implemented via Streamlit. Our testing involved 150 news articles covering each of the five languages named above. The results achieved were as follows; OCR extraction accuracy of 89.7%, Language Detection Accuracy of 94.2%, Summarisation Quality (ROUGE-L F1) of 92.1% Translation consistency of 91.4% and Sentiment Classification Accuracy of 88.6%. The average end-to-end processing time was between 10 and 16 seconds. Our analysis of Bharat Sum has revealed that it significantly outperforms previous single function systems by providing an Integrated, Real-Time Multilingual Processing capability which currently does not exist in this context. Bharat Sum has the potential to address significant gaps in the research literature regarding the analysis of Integrated Multilingual Media, and will likely serve as an economically viable solution for organisations conducting Digital Journalism, Media Monitoring, or Accessing Multilingual Information.
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.
V. M, Kunal Chakma, Anupam Jamatia et al.· ACM Transactions on Asian an...· 0 citations
The developed application is an example of the effective use of Artificial Intelligence in processing documents in regional languages and lays the groundwork for creating automated systems for document management.
Apoorva S., Usha B. S., S. Darshan· International Journal of Inn...· 0 citations
The rapid proliferation of digitally distributed news has made large-scale automated verification an urgent research priority, as deceptive content spreads across social platforms faster than manual reviewers can evaluate it. This work introduces a two-stage deep learning pipeline in which a pretrained BERT encoder generates rich contextual token embeddings that are subsequently processed by a Bidirectional Long Short-Term Memory (BiLSTM) network, enabling the classifier to exploit both sentence-level semantics and document-level narrative flow simultaneously. Experiments on the WELFake benchmark [1] following a standardised cleaning pipeline—lowercasing, URL stripping, tokenisation, stop-word removal, and WordNet lemmatisation— yielded 98.7 % accuracy and an F1-score of 0.986. This surpasses a BERT-only baseline by 3.1 percentage points and exceeds several previously published state-of-the-art results. The gains confirm that coupling transformer-based semantic representations with recurrent sequential modelling produces a measurable and reproducible improvement in misinformation detection. Planned extensions include multilingual evaluation and knowledge-distilled encoder replacements targeting real-time throughput constraints.
Ardra P Namboodiri, Archa P S, Honey Mol O· 2026 6th International Confe...· 0 citations
We present HelaBERT, a family of two BERT-based masked language models pre-trained from scratch on approximately 1 billion tokens of Sinhala text sourced from MADLAD-400, CulturaX, and a custom corpus comprising news articles, Sinhala Wikipedia, and web crawl data. HelaBERT-Small (~23.3M parameters, 6 layers) and HelaBERT-Large (~110M parameters, 12 layers) both use a SentencePiece Unigram tokenizer (vocabulary size 32,000) tailored to Sinhala's agglutinative morphology and complex script. We evaluate both models on four downstream Sinhala text classification tasks: news category classification, news source classification, sentiment analysis, and writing style classification, using 5 independent seed runs with stratified 80/20 train/test splits. We additionally propose a dual pooling classification head and evaluate it systematically across all four tasks, finding consistent improvements on sentiment analysis and a moderate gain on news category classification for HelaBERT-Small, while the standard [CLS]-linear head remains competitive on news source classification, a headline-level task with short average input length. We release both models to support further research in Sinhala NLP.
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.
R. Mundotiya· Annual International ACM SIG...· 0 citations
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.
Uri Katz, Omer Goldman, Tomasz Limisiewicz et al.· 0 citations