Automatic Keyphrase Extraction involves identifying essential phrases in a
document. These keyphrases are crucial in various tasks such as document
classification, clustering, recommendation, indexing, searching, summarization,
and text simplification. This paper introduces a platform that integrates
keyphrase datasets and facilitates the evaluation of keyphrase extraction
algorithms. The platform includes BibRank, an automatic keyphrase extraction
algorithm that leverages a rich dataset obtained by parsing bibliographic data
in BibTeX format. BibRank combines innovative weighting techniques with
positional, statistical, and word co-occurrence information to extract
keyphrases from documents. The platform proves valuable for researchers and
developers seeking to enhance their keyphrase extraction algorithms and advance
the field of natural language processing.

该论文介绍了一种平台，集成了关键短语数据集，促进了关键短语提取算法的评估，对于希望改进他们的关键短语提取算法并推进自然语言处理领域的研究人员和开发人员非常有价值。

BibRank：利用元数据的自动关键词提取平台

BibRank: Automatic Keyphrase Extraction Platform Using~Metadata

The SemEval-2010 benchmark dataset has brought renewed attention to the task
of automatic keyphrase extraction. This dataset is made up of scientific
articles that were automatically converted from PDF format to plain text and
thus require careful preprocessing so that irrevelant spans of text do not
negatively affect keyphrase extraction performance. In previous work, a wide
range of document preprocessing techniques were described but their impact on
the overall performance of keyphrase extraction models is still unexplored.
Here, we re-assess the performance of several keyphrase extraction models and
measure their robustness against increasingly sophisticated levels of document
preprocessing.

本文以 SemEval-2010 数据集为基础，重新评估了几个关键词提取模型的性能，并测量它们对越来越复杂的文档预处理水平的鲁棒性。