Summarization from Medical Documents: A Survey <s> Semi-structured databases <s> Considering the important part played by abstracts in the traditional information services, the possibility of producing abstracts by computer has not received very much attention. There are perhaps two main reasons for this. First, it appears tha t the production of well-constructed abstracts is an artificial intelligence problem, and therefore unlikely to be either feasible or worthwhile until well into the future: the alternative of picking sentences from here and there in a document is a rather unattractive proposition. Second, the cost of key-punching complete texts for input to an abstracting program can hardly be justified especially since the program will then in effect discard most of the text which has been so laboriously prepared. It now appears that the first of these objections is exaggerated --reasonable-looking abstracts can often be produced by quite 'unintelligent' programs -whi le with advances in technology the second problem should soon disappear. We should be ready to take advantage of this when it happens. Early work in this field concentrated on the extracting problem: that is to say, with finding sentences which could be extracted from a text to convey a good idea of its subject matter. Luhn (1958) write a program which looked for sentences containing clusters of 'key words': that is, the most frequent noncommonplace words in the text. The clusters were weighted according to their size and density, and those sentences containing the most highly weighted clusters were selected. At about the same time Baxendale (1958) drew attention to the fact that the position of a sentence within a text has a bearing on its importance: for instance, she showed that in 85 per cent of a sample of 200 paragraphs the 'topic' sentence was the first, while in another 7 per cent it was the last. Extending this idea, we can understand that the first few and the last few paragraphs of a document are likely to give a strong indication of its overall subject: the pages in between usually contain a lot of detail, which is not of much value taken out of its context. During the 1960s the most important work was carried out by Edmundson (1969), who studied four extracting methods, both individually and in all possible combinations. All four methods involved the assignment of weights to sentences, and the subsequent selection of sentences with the highest weights. The location method weighted sentences if they occurred in preferred positions <s> BIB001 </s> Summarization from Medical Documents: A Survey <s> Semi-structured databases <s> 11-Deoxy prostaglandin type compounds, i.e. prostaglandin type compounds in which the 11-hydroxy group is replaced by hydrogen, are disclosed, with processes for making them. These compounds are useful for a variety of pharmacological purposes, including anti-ulcer, inhibition of platelet aggregation, increase of nasal patency, and labor induction at term. <s> BIB002 </s> Summarization from Medical Documents: A Survey <s> Semi-structured databases <s> The Database of Interacting Proteins (DIP: http:// dip.doe-mbi.ucla.edu) is a database that documents experimentally determined protein–protein interactions. It provides the scientific community with an integrated set of tools for browsing and extracting information about protein interaction networks. As of September 2001, the DIP catalogs ∼11 000 unique interactions among 5900 proteins from >80 organisms; the vast majority from yeast, Helicobacter pylori and human. Tools have been developed that allow users to analyze, visualize and integrate their own experimental data with the information about protein–protein interactions available in the DIP database. <s> BIB003
A number of databases have been built to provide access to biomedical information, such as information about protein function and cellular pathways. More than 280 semi-structured databases exist currently BIB001 . Some examples are the following: • FlyBase: fruit fly genes and proteins The database of interacting proteins BIB002 Let us take DIP, for example, which provides an integrated set of tools for browsing and extracting information about protein interaction networks. As it is reported in BIB003 , DIP database is implemented as a relational database composed of four tables: protein table, interaction table, method table, reference table. The  reference table lists all the references to different articles that demonstrate protein interactions and link them to MEDLINE database. Therefore, a summarization system that exploits information from DIP should be able to employ DIP tools for browsing the database and even access relevant MEDLINE abstracts (see previous discussion) through the reference table.