Description of Invention
Text summarization has long existed, yet methods tailored to Islamic historical texts remain limited. These texts are rich in narrative and linguistic complexity, challenging current summarization techniques. This study addresses the gap by combining Named Entity Recognition (NER) and topic modeling with four NLP models—extractive (LexRank, HETFORMER) and abstractive (BART, PEGASUS). Models are assessed for their ability to summarize Islamic biographies while retaining key entities and thematic integrity. The best-performing model will power a web system for biographical extraction and summarization. This work enhances AI-assisted historical research, supporting accessible, context-rich understanding of Islamic history through domain-specific summarization techniques.