A Survey of Text Summarization Extractive Techniques <s> II. TEXT SUMMARIZATION EARLY HISTORY <s> Excerpts of technical papers and magazine articles that serve the purposes of conventional abstracts have been created entirely by automatic means. In the exploratory research described, the complete text of an article in machine-readable form is scanned by an IBM 704 data-processing machine and analyzed in accordance with a standard program. Statistical information derived from word frequency and distribution is used by the machine to compute a relative measure of significance, first for individual words and then for sentences. Sentences scoring highest in significance are extracted and printed out to become the "auto-abstract." <s> BIB001 </s> A Survey of Text Summarization Extractive Techniques <s> II. TEXT SUMMARIZATION EARLY HISTORY <s> This paper describes new methods of automatically extracting documents for screening purposes, i.e. the computer selection of sentences having the greatest potential for conveying to the reader the substance of the document. While previous work has focused on one component of sentence significance, namely, the presence of high-frequency content words (key words), the methods described here also treat three additional components: pragmatic words (cue words); title and heading words; and structural indicators (sentence location). The research has resulted in an operating system and a research methodology. The extracting system is parameterized to control and vary the influence of the above four components. The research methodology includes procedures for the compilation of the required dictionaries, the setting of the control parameters, and the comparative evaluation of the automatic extracts with manually produced extracts. The results indicate that the three newly proposed components dominate the frequency component in the production of better extracts. <s> BIB002 </s> A Survey of Text Summarization Extractive Techniques <s> II. TEXT SUMMARIZATION EARLY HISTORY <s> Introduced is a new approach to Information Retrieval developed on the basis of Hidden Markov Models (HMMs). HMMs are shown to provide a mathematically sound framework for retrieving documenta—documents with predefined boundaries and also entities of information that are of arbitrary lengths and formats (passage retrieval). Our retrieval model is shown to encompass promising capabilities: First, the position of occurrences of indexing features can be used for indexing. Positional information is essential, for instance, when considering phrases, negation, and the proximity of features. Second, from training collections we can derive automatically optimal weights for arbitrary features. Third, a query dependent structure can be determined for every document by segmenting the documents into passages that are either relevant or irrelevant to the query. The theoretical analysis of our retrieval model is complemented by the results of preliminary experiments. <s> BIB003 </s> A Survey of Text Summarization Extractive Techniques <s> II. TEXT SUMMARIZATION EARLY HISTORY <s> ●● ● ● ● To summarize is to reduce in complexity, and hence in length, while retaining some of the essential qualities of the original. This paper focusses on document extracts, a particular kind of computed document summary. Document extracts consisting of roughly 20% of the original cart be as informative as the full text of a document, which suggests that even shorter extracts may be useful indicative summmies. The trends in our results are in agreement with those of Edmundson who used a subjectively weighted combination of features as opposed to training the feature weights using a corpus. <s> BIB004
Interest in automatic text summarization, arose as early as the fifties. An important paper of these days is the one in 1958, suggested to weight the sentences of a document as a function of high frequency words BIB001 , disregarding the very high frequency common words. Automatic text summarization system BIB002 in 1969, which, in addition to the standard keyword method (i.e., frequency depending weights), also used the following three methods for determining the sentence weights: 1. Cue Method: This is based on the hypothesis that the relevance of a sentence is computed by the presence or absence of certain cue words in the cue dictionary. 2. Title Method: Here, the sentence weight is computed as a sum of all the content words appearing in the title and (sub-) headings of a text. 3. Location Method: This method is based on the assumption that sentences occurring in initial position of both text and individual paragraphs have a higher probability of being relevant. The results showed, that the best correlation between the automatic and human-made extracts was achieved using a combination of these three latter methods. The Trainable Document Summarizer BIB004 in 1995 performs sentence extracting task, based on a number of weighting heuristics. Following features were used and evaluated: 1. Sentence Length Cut-O Feature: sentences containing less than a pre-specified number of words are not included in the abstract 2. Fixed-Phrase Feature: sentences containing certain cue words and phrases are included 3. Paragraph Feature: this is basically equivalent to Location Method feature in BIB002 4. Thematic Word Feature: the most frequent words are defined as thematic words. Sentence scores are functions of the thematic words' frequencies 5. Uppercase Word Feature: upper-case words (with certain obvious exceptions) are treated as thematic words, as well. A Corpus was used in this method, which contained 188 document/summary pairs from 21 publications in a scientific/technical domain. The summaries were produced by professional experts and the sentences occurring in the summaries were aligned to the original document texts, indicating also the degree of similarity as mentioned earlier, the vast majority (about 80%) of the summary sentences could be classified as direct sentence matches. The ANES text extraction system  in 1995 is a system that performs automatic, domain-independent condensation of news data. The process of summary generation has four major constituents: 1. Corpus analysis: this is mainly a calculation of the tf*idf -weights for all terms 2. Statistical selection of signature words: terms with a high tf*idf-weight plus headline-words 3. Sentence weighting: summing over all signature word weights, modifying the weights by some other factors, such as relative location 4. Sentence selection: Selecting high scored sentences. Hidden Markov Models (HMMs) BIB003 : As prove to be a mathematically sound frame-work for document retrieval. If one approaches the task of text abstracting from such a probabilistic modeling perspective, it might well be possible that HMMs could be employed for this purpose, as well. Clustering: Building links  and/or clusters between index terms, phrases and/or other subparts of the documents has been employed by standard information retrieval. Although this is not an issue in any of the above mentioned abstracting systems, it seems to be worth of consideration when building such systems. 