Gerard Salton

Term-weighting approaches in automatic text retrieval

The experimental evidence accumulated over the past 20 years indicates that textindexing systems based on the assignment of appropriately weighted single terms produce retrieval results that are superior to those obtainable with other more elaborate text representations. These results depend crucially on the choice of effective term weighting systems. This paper summarizes the insights gained in automatic term weighting, and provides baseline single term indexing models with which other more elaborate content analysis procedures can be compared.

Improving retrieval performance by relevance feedback

Relevance feedback is an automatic process, introduced over 20 years ago, designed to produce query formulations following an initial retrieval operation. The principal relevance feedback methods described over the years are examined briefly, and evaluation data are included to demonstrate the effectiveness of the various methods. Prescriptions are given for conducting text retrieval operations iteratively using relevance feedback.

Developments in automatic text retrieval.

Recent developments in the storage, retrieval, and manipulation of large text files are described. The text analysis problem is examined, and modern approaches leading to the identification and retrieval of selected text items in response to search requests are discussed.

Computer Evaluation of Indexing and Text Processing

Automatic indexing methods are evaluated and design criteria for modern information systems are derived.

Automatic text structuring and summarization

Abstract In recent years, information retrieval techniques have been used for automatic generation of semantic hypertext links. This study applies the ideas from the automatic link generation research to attack another important problem in text processing—automatic text summarization. An automatic “general purpose” text summarization tool would be of immense utility in this age of information overload. Using the techniques used (by most automatic hypertext link generation algorithms) for inter-document link generation, we generate intra-document links between passages of a document. Based on the intra-document linkage pattern of a text, we characterize the structure of the text. We apply the knowledge of text structure to do automatic text summarization by passage extraction. We evaluate a set of fifty summaries generated using our techniques by comparing them to paragraph extracts constructed by humans. The automatic summarization methods perform well, especially in view of the fact that the summaries generated by two humans for the same article are surprisingly dissimilar.

Research and Development in Information Retrieval

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A Theory of Term Importance in Automatic Text Analysis

Most existing automatic content analysis and indexing techniques are based on word frequency characteristics applied largely in an ad hoc manner. Contradictory requirements arise in this connection, in that terms exhibiting high occurence frequencies in individual documents are often useful for high recall performance (to retrieve many relevant items), whereas terms with low frequency in the whole collection are useful for high precision (to reject nonrelevant items).

The effect of adding relevance information in a relevance feedback environment

The effects of adding information from relevant documents are examined in the TREC routing environment. A modified Rocchio relevance feedback approach is used, with a varying number of relevant documents retrieved by an initial SMART search, and a varying number of terms from those relevant documents used to expand the initial query. Recall-precision evaluation reveals that as the amount of expansion of the query due to adding terms from relevant documents increases, so does the effectiveness. There appears to be a linear relationship between the log of the number of terms added and the recall-precision effectiveness. There also appears to be a linear relationship between the log of the number of known relevant documents and the recall-precision effectiveness.

Another look at automatic text-retrieval systems

Evidence from available studies comparing manual and automatic text-retrieval systems does not support the conclusion that intellectual content analysis produces better results than comparable automatic systems.

Automatic analysis, theme generation, and summarization of machine-readable texts

Vast amounts of text material are now available in machine-readable form for automatic processing. Here, approaches are outlined for manipulating and accessing texts in arbitrary subject areas in accordance with user needs. In particular, methods are given for determining text themes, traversing texts selectively, and extracting summary statements that reflect text content.