Identifying Repeated Sections within Documents

Girish K. Palshikar, Sachin Pawar, Rajiv Srivastava, Mahek Shah


Identifying sections containing a logically coherent text about a particular aspect is important for fine-grained IR, question-answering and information extraction. We propose a novel problem of identifying repeated sections, such as project details in resumes and different sports events in the transcript of a news broadcast. We focus on resumes and present four techniques (2 unsupervised, 2 supervised) for automatically identifying repeated project sections. The knowledge-based method is modeled after the human way closely. The other methods are based on integer linear programming and sequence labeling. The proposed techniques are general and can be used for identifying other kinds of repeated sections (and even non-repeating sections) in different types of documents. We compared the four methods on a dataset of resumes of IT professionals and also evaluated the benefits of identifying such repeated sections in practical IR tasks. To the best of our knowledge, this paper is the first to propose and solve the problem of repeated sections identification.


Section identification, fine-grained IR, resume searching

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