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Interpretable Aspect-Aware Capsule Network for Peer Review Based Citation Count Prediction

Published: 24 November 2021 Publication History

Abstract

Citation count prediction is an important task for estimating the future impact of research papers. Most of the existing works utilize the information extracted from the paper itself. In this article, we focus on how to utilize another kind of useful data signal (i.e., peer review text) to improve both the performance and interpretability of the prediction models.
Specially, we propose a novel aspect-aware capsule network for citation count prediction based on review text. It contains two major capsule layers, namely the feature capsule layer and the aspect capsule layer, with two different routing approaches, respectively. Feature capsules encode the local semantics from review sentences as the input of aspect capsule layer, whereas aspect capsules aim to capture high-level semantic features that will be served as final representations for prediction. Besides the predictive capacity, we also enhance the model interpretability with two strategies. First, we use the topic distribution of the review text to guide the learning of aspect capsules so that each aspect capsule can represent a specific aspect in the review. Then, we use the learned aspect capsules to generate readable text for explaining the predicted citation count. Extensive experiments on two real-world datasets have demonstrated the effectiveness of the proposed model in both performance and interpretability.

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  1. Interpretable Aspect-Aware Capsule Network for Peer Review Based Citation Count Prediction

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    cover image ACM Transactions on Information Systems
    ACM Transactions on Information Systems  Volume 40, Issue 1
    January 2022
    599 pages
    ISSN:1046-8188
    EISSN:1558-2868
    DOI:10.1145/3483337
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    Publication History

    Published: 24 November 2021
    Accepted: 01 May 2021
    Revised: 01 March 2021
    Received: 01 August 2020
    Published in TOIS Volume 40, Issue 1

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    1. Citation count prediction
    2. peer review
    3. capsule network

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    • National Natural Science Foundation of China
    • Beijing Academy of Artificial Intelligence (BAAI)
    • Beijing Outstanding Young Scientist Program
    • Fundamental Research Funds for the Central Universities
    • Research Funds of Renmin University of China
    • Alibaba Group

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    • (2023)Learning to Relate to Previous Turns in Conversational SearchProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599411(1722-1732)Online publication date: 6-Aug-2023
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