Utility of Missing Concepts in Query-biased Summarization

Utility of Missing Concepts in Query-biased Summarization
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缺失概念在查询偏向摘要中的效用

DOI:
10.1145/3404835.3463121
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发表时间:
2021
期刊:
Proceedings of The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 21
影响因子:
--
通讯作者:
Allan, James
Allan, James
中科院分区:
--
文献类型:
--
作者:
Sarwar, Sheikh Muhammad;Moraes, Felipe;Jiang, Jiepu;Allan, James

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基于查询的摘要(QBS)旨在为检索到的文档生成依赖于查询的摘要,以减少检查全文内容的人工工作量。典型的摘要方法提取与查询重叠的文档片段,并将其显示给搜索者。这种QBS方法在文档中显示相关信息,但不通知搜索者缺少什么。我们的研究重点是通过暴露查询中在检索结果中缺失的信息来减少用户查找相关文档的工作量。我们使用一种经典的方法DSPApprox来查找与查询相关的术语或短语。然后,我们识别文档中缺少的术语或短语,将它们呈现在搜索界面上,并让群组工作人员根据片段和缺少的信息来判断文档的相关性。实验结果表明,与传统的只显示相关片段的方法相比,该方法既有优势,也有局限性。
Query-biased Summarization (QBS) aims to produce a query-dependent summary of a retrieved document to reduce the human effort for inspecting the full-text content. Typical summarization approaches extract document snippets that overlap with the query and show them to searchers. Such QBS methods show relevant information in a document but do not inform searchers what is missing. Our study focuses on reducing user effort in finding relevant documents by exposing the information in the query that is missing in the retrieved results. We use a classical approach, DSPApprox, to find terms or phrases relevant to a query. Then, we identify which terms or phrases are missing in a document, present them in a search interface, and ask crowd workers to judge document relevance based on snippets and missing information. Experimental results show both benefits and limitations of our method compared with traditional ones that only show relevant snippets.
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