The end users of medical digital libraries need quick access to information that is specific to the patients under their care. We present a summarization system that finds and extracts results from multiple medical journal articles returned by a search, filters results that match the patient and merges and orders the remaining facts for the summary. Our approach features an integration of text categorization, information extraction, information fusion and text reformulation for the summarization task. 1

Objectives: We present the summarization system in the PERSIVAL medical digital library. Although we discuss the context of our summarization research within the PERSIVAL platform, the primary focus of this article is on strategies to define and generate customized summaries.

Despite the large amount of online medical literature, it can be difficult for clinicians to find relevant information at the point of patient care. In this paper, we present techniques to personalize the results of search, making use of the online patient record as a sophisticated, pre-existing user model. Our work in PERSIVAL, a medical digital library, includes methods for re-ranking the results of search to prioritize those that better match the patient record. It also generates summaries of the re-ranked results which highlight information that is relevant to the patient under the physician’s care. We focus on the use of a common representation for the articles returned by search and the patient record which facilitates both the re-ranking and the summarization tasks.This common approach to both tasks has a strong positive effect on the ability to personalize information. 1.

In this thesis, we present a user-sensitive approach to text summarization. One domain which would highly benefit from 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, be-cause our input is textual and not semantic, new challenges arise. We operate over

Despite the large amount of online medical literature, it can be difficult for clinicians to find relevant information at the point of patient care. In this paper, we present techniques to personalize the results of search, making use of the online patient record as a sophisticated, pre-existing user model. Our work in PERSIVAL, a medical digital library, includes methods for re-ranking the results of search to prioritize those that better match the patient record. It also generates summaries of the re-ranked results which highlight information that is relevant to the patient under the physician's care. We focus on the use of a common representation for the articles returned by search and the patient record which facilitates both the re-ranking and the summarization tasks.This common approach to both tasks has a strong positive effect on the ability to personalize information. 1.