Abstract
The summarization of scientific articles and particularly their related work sections would support the researchers in their investigation by allowing them to summarize a large number of articles. Scientific articles differ from generic text due to their specific structure and inclusion of citation sentences. Related work sections of scientific articles generally describe the most important facts of prior related work. Automatically summarizing these sections would support research development by speeding up the research process and consequently enhancing research quality. However, these sections may overlap syntactically and semantically. This research proposes to explore the automatic summarization of multiple related work sections. More specifically, the research goals of this work are to reduce the redundancy of citation sentences and enhance the readability of the generated summary by investigating a semantic graph-based approach and cross-document structure theory. These approaches have proven successful in the field of abstractive document summarization.
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Altmami, N.I., Menai, M.E.B. (2018). Semantic Graph Based Automatic Summarization of Multiple Related Work Sections of Scientific Articles. In: Agre, G., van Genabith, J., Declerck, T. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2018. Lecture Notes in Computer Science(), vol 11089. Springer, Cham. https://doi.org/10.1007/978-3-319-99344-7_23
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DOI: https://doi.org/10.1007/978-3-319-99344-7_23
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