User-sensitive text summarization: application to the medical domain

• Authors: Kathleen Mckeown;Noemie Elhadad
• Venue: User-sensitive text summarization: application to the medical domain
• Year: 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.