Summarization from Medical Documents: A Survey <s> Extractive multi-document summarization <s> The task of creating indicative summaries that help a searcher decide whether to read a particular document is a difficult task. This paper examines the indicative summarization task from a generation perspective, by first analyzing its required content via published guidelines and corpus analysis. We show how these summaries can be factored into a set of document features, and how an implemented content planner uses the topicality document feature to create indicative multidocument query-based summaries. <s> BIB001 </s> Summarization from Medical Documents: A Survey <s> Extractive multi-document summarization <s> ABSTRACT In this paper, we propose the use of multido ument summarization as a post-pro essing step in do ument retrieval. We examine the use of the summary as a repla ement to the standard ranked list. The form of the summary is novel be ause it has both informative and indi ate elements, designed to help di erent users perform their tasks better. Our summary uses the do uments' topi al stru ture as a ba kbone for its own stru ture, as it was deemed the most useful do ument feature in our study of a orpus of summaries. <s> BIB002
Although the production of summaries from multiple documents is usually done with abstractive techniques, BIB001 BIB002 follow a different approach. They argue that different types of summaries, such as indicative or informative, serve different informational purposes and both can be useful, and that extracting sentences for the creation of an informative multi-document summary "is well accepted since it is simple, fast and easy to evaluate". Their system, Centrifuser, which is the summarization engine of the PERSIVAL (PErsonilized Retrieval and Summarization of Image, Video and Language) project  , produces both indicative and informative multi-document summaries, with the aim of highlighting the similarities and differences among the documents. The input to the Centrifuser is articles, retrieved by the search engine of the PERSIVAL system according to the patient record and the user query. For each article they create a topic tree which depicts the sectioning of each article. A composite topic tree is then created by merging together all the topic trees and adding details to each node such as relative typicality (i.e. how typical is that topic compared to the rest of the topics), position within the article, and various lexical forms in which it may be expressed BIB001 . In the next step they try to match the nodes of the topic trees with the query. The matched nodes do not contain any text, but instead they point to sections in the original documents, from which the most representative sentences should be extracted. Since the compression rate posed will not always allow for each topic to receive a sentence, the first step is to choose which topics are going to receive a sentence. In the next step they choose for each topic the representative sentences. The final step for the creation of the summary involves the ordering of those sentences, which is achieved by first ordering the topics according to each topic's typicality, and then ordering the sentences themselves inside every topic, according to the physical position of every sentence.