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- W4284694517 abstract "Identifying academic experts is crucial for the progress of science, enabling researchers to connect, form networks, and collaborate on the most pressing research problems. A key challenge for ranking experts in response to a query is how to infer their expertise from the publications they coauthored. Profile-centric approaches represent candidate experts by concatenating all their publications into a text-based profile. Despite offering a complete picture of each candidate's scientific output, such lengthy profiles make it inefficient to leverage state-of-the-art neural architectures for inferring expertise. To overcome this limitation, we investigate the suitability of extractive summarization as a mechanism to reduce candidate profiles for semantic encoding using Transformers. Our thorough experiments with a representative academic search test collection demonstrate the benefits of encoding summarized profiles for an improved expertise inference." @default.
- W4284694517 created "2022-07-08" @default.
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- W4284694517 date "2022-07-06" @default.
- W4284694517 modified "2023-10-14" @default.
- W4284694517 title "On Extractive Summarization for Profile-centric Neural Expert Search in Academia" @default.
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- W4284694517 doi "https://doi.org/10.1145/3477495.3531713" @default.
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