论文标题
将基于变压器的文本摘要应用于键形生成
Applying Transformer-based Text Summarization for Keyphrase Generation
论文作者
论文摘要
键形对于搜索和系统化学术文档至关重要。大多数用于键形提取的方法是针对文本中最重要单词的提取。但是实际上，密钥词的列表通常包含明确出现在文本中的单词。在这种情况下，键形列表表示源文本的抽象摘要。在本文中，我们使用基于流行的变压器的模型来尝试使用四个基准数据集进行键形摘要，以进行抽象文本摘要。我们将获得的结果与常见的无监督和监督方法的结果进行了比较。我们的评估表明，摘要模型可以根据全匹配的F1得分和BertScore的术语生成键形词。但是，它们产生的许多单词在作者的键形列表中不存在，这使得摘要模型在Rouge-1方面无效。我们还研究了几种订购策略来连接目标钥匙串。结果表明，策略的选择会影响键形生成的性能。
Keyphrases are crucial for searching and systematizing scholarly documents. Most current methods for keyphrase extraction are aimed at the extraction of the most significant words in the text. But in practice, the list of keyphrases often includes words that do not appear in the text explicitly. In this case, the list of keyphrases represents an abstractive summary of the source text. In this paper, we experiment with popular transformer-based models for abstractive text summarization using four benchmark datasets for keyphrase extraction. We compare the results obtained with the results of common unsupervised and supervised methods for keyphrase extraction. Our evaluation shows that summarization models are quite effective in generating keyphrases in the terms of the full-match F1-score and BERTScore. However, they produce a lot of words that are absent in the author's list of keyphrases, which makes summarization models ineffective in terms of ROUGE-1. We also investigate several ordering strategies to concatenate target keyphrases. The results showed that the choice of strategy affects the performance of keyphrase generation.
