Automatic Indexing and Abstracting of Document Texts
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Topical relevance is a necessary, but not sufficient condition for relevance Froelich, Topical relevance usually acts as a first filter in selecting documents Boyce, It is the easiest relevance factor to deal with in text-based systems and it is the major factor when ranking documents according to their relevance to the query in current information retrieval systems.
Relevance is difficult to compute in exact numbers. Relevance is assessed by people in abstract relative terms e. However, performance of information retrieval systems is usually measured in terms of effectiveness metrics, i. Recall measures the proportion of relevant documents retrieved and precision the proportion of retrieved documents that are relevant Salton, , p.
Or, the query is expressed as a natural language utterance, which is automatically indexed to provide the necessary key terms for document matching. This is, however, a poor representation of the real information need. An information need situation encompasses all factors that the user brings to the situation: previous knowledge, awareness of information that is available, affective and emotional factors, the expected use of the information, and other personal and situational factors. Even, when the need is more or less adequately expressed in natural language, its representation is usually reduced to some key terms, which insufficiently represent the real need.
Automatic Indexing and Abstracting of Document Texts | Marie-Francine Moens | Springer
Moreover, the information need situation is dynamic and constantly changing Barry, Sometimes, the user of a document database does not have a well-defined need. He or she wishes to skim through the database. Or more strongly, a document only becomes of great importance after completely reading it Allen, It is very hard to correctly and adequately conceptualize and represent the real information need of a person at a given time.
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Nevertheless, given the large number of documents in current document bases, information selection 16 Chapter 1 is necessary. The user does not want to read the fill-text of every document in the collection to satisfy his or her information need. The process of information retrieval consists of several probabilistic operations cf. Blair, , p. Second, the natural language understanding of the document text is poor, and often yields an incomplete or incorrect characterization of the text and of its aboutness.
Finally, the matching between query and document is a probabilistic operation. Documents are usually ranked according to their probability of relevance to the query. The matching is commonly restricted to a term matching between query and document, whereby the probability of relevance is proportional to the number of matched terms cf.
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The above problem regards a classical information retrieval system. However, the information problem is also present in browsing systems and in question-answering systems. In browsing systems, the user does not make his information need explicit. Then, the information problem regards an inadequate selection of documents due to an incorrect or incomplete characterization of the texts and their aboutness.
In questionanswering systems, the information need is clearly stated question for specific information. Here again, the information problem regards the oftenfaulty characterization of the document content. The Need for Indexing and Abstracting Texts 7. This approach contrasts with searching collections that have fixed descriptors attached to the document texts. The original idea Swanson, was positively tested by Salton and since then implementation of full-text retrieval gained more and more success. Today, the full-text segment is still a growing section of the commercial computerized database market Sievert, Full-text search is attractive for many reasons and has some definite advantages.
Digital technology provides cheap storage for full-text and supplies fast computational technology making searching of full-text efficient. It is also very convenient to search different text types in large document collections just by searching individual words. Additionally, as it employs a simple form of automatic indexing, it avoids the need for human indexers, whose employment is increasingly costly and whose work often appears inconsistent and less fully effective.
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Full-text search is a first attempt to transfer indexing from a primarily a priori process, to a process determined by specific information needs and other situational factors Tenopir, ; Salton, Fixed text descriptors severely hamper the accessibility of the texts. Sometimes documents are not retrievable relying on assigned descriptors, because their information value to the users is peripheral to their main focus. Indexing of concepts and terms in a full-text search is situation dependent and would be performed according the requirements of each incoming request.
Inexperienced users found that searching with natural language terms in the full-text was easier than searching with fixed text descriptors Tenopir, Still, full-text search is not a magical formula and it suffers from shortcomings. The occurrence of a word or word combination is no guarantee for relevance. This is currently the case with full-text searches on the Internet. Also recall may suffer. A survey by Croft, Krovetz, and Turtle indicates that users often query documents in terms that they are familiar with, and these terms are frequently not the terms used in the document itself.
If the occurrences of these terms in a relevant document are independent events, the probability of finding documents that contain the exact term combination decreases as the number of search terms in the combination increases. Because an ideal query representation cannot be generated without knowing a great deal about the composition of the document collection, it is customary to conduct searches iteratively, first operating with a tentative query formulation, and then improving The Need for Indexing and Abstracting Texts 19 formulations for subsequent searches based on the evaluations of previously retrieved materials.
One method for automatically generating improved query formulations is the well-known relevance feedback process. Methods using relevance information have been studied for decades and are still investigated. Rocchio was the first to experiment with query modification and with positive results. Salton and Buckley compared this work across different test collections. The main assumption behind relevance feedback is that documents relevant to a particular query resemble each other.
This implies that, when a retrieved document has been identified as relevant to a given query, the query formulation can be improved by increasing its similarity to such a previously retrieved relevant item. The reformulated query is expected to retrieve additional relevant items that are similar to the originally identified relevant item. Analogously, by reformulating the query, its similarity with retrieved non-relevant documents can be decreased. So, a better query is learned by judging retrieved documents as relevant or non-relevant.
The original query can be altered in two substantial ways Salton, , p. Second, using the occurrence characteristics of the terms in the previously retrieved relevant and nonrelevant documents of the collection allows altering the weight of the original query terms.
The weight or importance of query terms occurring in relevant documents is increased. Analogously, terms included in previously retrieved non-relevant documents could be de-emphasized. Experiments indicate that performing multiple iterations of feedback until the user is completely satisfied with the results, is highly desirable. Relevance feedback is used both in ad-hoc interactive information retrieval and document filtering based on long-term information needs. Although relevance feedback is considered as being effective in improving retrieval performance, there are still some obstacles.
Moreover, current text collections often contain large documents that span several subject areas.
Automatic Indexing and Abstracting of Document Texts
It has been shown that trimming large documents by selecting a good passage when selecting index terms, has a positive impact on feedback effectiveness Allan, Shoham, There is an emerging interest in the engagement of information agents Croft, ; Standera, , p. An information agent supplies a user with relevant information that is for instance drawn from a collection of documents.
However, there is a growing interest in agents that identify or learn appropriate content attributes of texts. A typical task in an information retrieval environment is filtering of information according to a profile of a user or a class of users Allen, The knowledge in the profile is intellectually acquired from the user and experts , implemented and maintained by knowledge engineers, Or, the knowledge is learned by the agent itself based on good positive and negative training examples.
Again, such an approach assumes the relevancy of documents that are similar to previously retrieved documents found relevant. Information agents also perform other functions, which support the retrieval operation. An agent can also select the best search engine based upon knowledge of search techniques. Research on information agents especially focuses upon the characterization and refinement of the information need.
It is equally important to automatically identify or learn appropriate content attributes of texts Maes, Electronic documents become more complex, They are bestowed with attributes, which form a document description.
Also the linguistic text message in an electronic medium is structured and delivered distinctively from the print and paper medium McArthur, Texts have stylistic attributes e. Salton, , p. These attributes are recognizable by their mark-ups in the document. Different standards for document description allow using the documents and their attributes independent of the hardware and the application software. The use of such mark-ups greatly benefits the accessibility of the information contained in and attached to the documents.
Despite the appeal and promise of such an approach, one must be aware of its limits among which the complexity and cost of assigning the mark-ups. The creation of current and future electronic documents is sometimes compared with the creation of software Walker, Hence, the term document engineering is in use. Creation of electronic documents is a complex task. Compared to the field of software engineering there is a clear need for modularity, abstraction, and consistency. When mark-ups regard content attributes e.
The intellectual assignment of content mark-up is considered as a form of manual indexing Croft et al. Multiple studies indicate that manual indexing is inconsistent and subjective Beghtol, ; Collantes, A study of Ellis, Furner-Hines, and Willett 1 shows little similarity between the link-sets inserted by different persons in a set of full-text documents.
These authors were not able to prove a positive relationship between inter-linker 22 Chapter 1 consistency and navigational effectiveness in hypertext systems Ellis et al.