인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Abstract The exponential growth of Arabic digital content has intensified the challenge of information overload, highlighting the need for effective Automatic Text Summarization (ATS) techniques. Existing graph-based approaches, such as TextRank and LexRank, demonstrate promising results but face notable limitations in Arabic, including shallow semantic modeling, neglect of sentence relevance, structural bias, and slow convergence. To address these challenges, this work introduces a hybrid graph–feature-based framework with four key innovations: (i) a refined graph construction strategy that integrates semantic features and sentence relevance into edge weights, (ii) a novel ranking algorithm, ST-Rank, that incorporates semantic information, achieves faster convergence through informed initialization, and leverages two new normalization strategies, (iii) a hybrid fusion mechanism that combines graph-based scores with semantic and statistical sentence-level attributes, and (iv) the design of novel feature sets to enhance sentence salience estimation. Extensive experiments on the Essex Arabic Summaries Corpus (EASC) demonstrate that the proposed approach surpasses current state-of-the-art summarization methods, achieving ROUGE-1 and ROUGE-2 F1-scores of 0.705 and 0.651, respectively, with significant gains over baseline and related techniques. Furthermore, ST-Rank exhibits markedly improved computational efficiency, converging up to five times faster than conventional graph-based methods. These results confirm the effectiveness and robustness of the proposed framework in advancing Arabic text summarization.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
최근 본 자료 전체보기
