To help analysts sift through large numbers of documents, we suggest an auto-highlighting system that computationally identifies the topmost salient sentences in each document as a form of summary and rapid comprehens...
详细信息
ISBN:
(纸本)9781467362139;9781467362146
To help analysts sift through large numbers of documents, we suggest an auto-highlighting system that computationally identifies the topmost salient sentences in each document as a form of summary and rapid comprehension aid. We conducted a user study to gather data about the types of sentences people highlight when reading and comprehending text. Our study focuses not only on the comparison between expert and non-expert users for different document types, but also the comparison between users and common algorithmic metrics for sentence selection. We analyze user-defined categories for describing the variations in the types of highlighted sentences as well as insight concerning rhetoric and language that could strengthen future algorithms.
To help analysts sift through large numbers of documents, we suggest an auto-highlighting system that computationally identifies the topmost salient sentences in each document as a form of summary and rapid comprehens...
详细信息
ISBN:
(纸本)9781467362146
To help analysts sift through large numbers of documents, we suggest an auto-highlighting system that computationally identifies the topmost salient sentences in each document as a form of summary and rapid comprehension aid. We conducted a user study to gather data about the types of sentences people highlight when reading and comprehending text. Our study focuses not only on the comparison between expert and non-expert users for different document types, but also the comparison between users and common algorithmic metrics for sentence selection. We analyze user-defined categories for describing the variations in the types of highlighted sentences as well as insight concerning rhetoric and language that could strengthen future algorithms.
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