Back
  1. Home
  2. Profile of Keerthana MURUGARAJ (Unilu)
Profil

MURUGARAJ Keerthana

University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)

ORCID
0009-0008-5100-055X
Main Referenced Co-authors
LAMSIYAH, Salima  (7)
THEOBALD, Martin  (5)
DURING, Marten  (3)
SCHOMMER, Christoph  (2)
Main Referenced Keywords
Comparative Study (1); Computer Science - Artificial Intelligence (1); Computer Science - Computation and Language (1); Computer Science - Information Retrieval (1); Extractive Text Summarization (1);
Main Referenced Unit & Research Centers
Luxembourg Centre for Contemporary and Digital History (C2DH) > Digital History & Historiography (DHI) (3)
Main Referenced Disciplines
Computer science (8)

Publications (total 8)

The most downloaded
167 downloads
K. MURUGARAJ, S. LAMSIYAH, M. DURING, and M. THEOBALD. "Mining the Past: A Comparative Study of Classical and Neural Topic Models on Historical Newspaper Archives." In Association for Computational Linguistics. Albuquerque, United States: Association for Computational Linguistics, 2025. https://hdl.handle.net/10993/64861

The most cited

6 citations (OpenAlex)

K. MURUGARAJ, S. LAMSIYAH, and C. SCHOMMER. "Abstractive Summarization of Historical Documents: A New Dataset and Novel Method Using a Domain-Specific Pretrained Model." IEEE Access, 13 (2025): 10918-10932. doi:10.1109/access.2025.3528733 https://hdl.handle.net/10993/63750

The most significant

K. MURUGARAJ, S. LAMSIYAH, and M. THEOBALD. "RAGVUE: A Diagnostic View for Explainable and Automated Evaluation of Retrieval-Augmented Generation." Paper presented at Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 3: System Demonstrations), March 2026.
Peer reviewed


Scientific outputs

Articles in scientific journals with peer reviewing verified by ORBi or included in HEC journal guide

K. MURUGARAJ, S. LAMSIYAH, M. DURING, and M. THEOBALD. "Topic-RAG for Historical Newspapers: Enhancing Information Retrieval in Humanities Research through Topic-Based Retrieval-Augmented Generation." Computational Humanities Research (2025): 1-21. doi:10.1017/chr.2025.10018
Peer reviewed

K. MURUGARAJ, S. LAMSIYAH, and C. SCHOMMER. "Abstractive Summarization of Historical Documents: A New Dataset and Novel Method Using a Domain-Specific Pretrained Model." IEEE Access, 13 (2025): 10918-10932. doi:10.1109/access.2025.3528733
Peer Reviewed verified by ORBi

Proceedings published in a book or a journal

K. MURUGARAJ, S. LAMSIYAH, M. DURING, and M. THEOBALD. "Mining the Past: A Comparative Study of Classical and Neural Topic Models on Historical Newspaper Archives." In Association for Computational Linguistics. Albuquerque, United States: Association for Computational Linguistics, 2025.
Peer reviewed

S. LAMSIYAH, K. MURUGARAJ, and C. SCHOMMER. "Historical-Domain Pre-trained Language Model for Historical Extractive Text Summarization." In Historical-Domain Pre-trained Language Model for Historical Extractive Text Summarization. London, United Kingdom: https://avestia.com/, 2023. doi:10.11159/cist23.152
Peer reviewed

Eprint/Working paper

K. MURUGARAJ, S. LAMSIYAH, M. DURING, and M. THEOBALD. "Automating Historical Insight Extraction from Large-Scale Newspaper Archives via Neural Topic Modeling." Eprint/Working Paper, 2025. https://orbilu.uni.lu/handle/10993/66919.

Unpublished scientific communications

Scientific congresses and symposiums with national audience

K. MURUGARAJ. "RAGVUE: A Diagnostic View for Explainable and Automated Evaluation of Retrieval-Augmented Generation." Paper presented at Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 3: System Demonstrations), March 2026.
Peer reviewed

K. MURUGARAJ, S. LAMSIYAH, and M. THEOBALD. "RAGVUE: A Diagnostic View for Explainable and Automated Evaluation of Retrieval-Augmented Generation." Paper presented at Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 3: System Demonstrations), March 2026.
Peer reviewed

K. MURUGARAJ, S. LAMSIYAH, and M. THEOBALD. "ExpandFuse: A Hybrid Retrieval Framework with Query Expansion and Topic-Aware Reranking for Multi-Hop Question Answering." Paper presented at 13th IEEE International Conference on Big Data (IEEE BigData 2025), 08 December 2025. doi:10.1109/bigdata66926.2025.11402490
Peer reviewed

Contact ORBilu