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作者 Elhadad, Noemie
書名 User-sensitive text summarization: Application to the medical domain
國際標準書號 9780542524165
book jacket
說明 200 p
附註 Source: Dissertation Abstracts International, Volume: 67-02, Section: B, page: 0976
Adviser: Kathleen McKeown
Thesis (Ph.D.)--Columbia University, 2006
In this thesis, we present a user-sensitive approach to text summarization. One domain which would highly benefit front tailoring summaries to both individual and class-based user characteristics is the medical domain, where physicians and patients access similar information, each with their own needs and abilities. Our framework is a medical digital library for physicians and patients. We describe a summarizer, which generates summaries of findings in an input set of clinical studies. When a physician is treating a specific patient, he's looking for information relevant to the patient's history and problems. The summarizer takes the user's interests into account and presents only the findings pertaining to a user model, as approximated by an existing patient record. The same synthesis of information can also be of interest to the patient. The summarizer predicts which medical terms used in a text will be too technical for patients, and augments it, with appropriate definitions when necessary
We adopt a generation-like architecture for our summarizer. However, because our input is textual and not semantic, new challenges arise. We operate over a content representation hybrid between full-semantic and extracted phrases. Our content organization strategy is dynamic and data-driven. This is in contrast to most summarizers which use no explicit strategies to order information extracted from several input documents. The result is more readable, coherent output. To generate the actual summary, the summarizer makes use of aggregation and phrasal generation. The result is a concise and fluent summary
One key challenge when it comes to adapting a text for a different audience is identifying the bottleneck for reader comprehension. We analyzed corpora of technical and lay medical texts and qualified differences. We identified the presence of difficult vocabulary as the major obstacle to comprehension for lay readers. We designed an unsupervised method to predict which terms are incomprehensible for lay readers and provide the user with appropriate definitions
Our methods are grounded on corpus analyses and feasibility studies conducted with physicians and consumers of health information. To assess the value of our work, we evaluated our summarizer both intrinsically and extrinsically. Our task-based evaluation conducted with physicians at the ICU demonstrates that personalized summaries help physicians access relevant information better than generic summaries. Evaluation with lay readers shows that our method to augment technical medical texts improves readers' comprehension significantly
School code: 0054
DDC
Host Item Dissertation Abstracts International 67-02B
主題 Engineering, Biomedical
Computer Science
0541
0984
Alt Author Columbia University
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