A Survey of Text Summarization Extractive Techniques
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paper_title: Optimizing Text Summarization Based on Fuzzy Logic

paper_content:

In this paper we first analyze some state of the art methods for text summarization. We discuss what the main disadvantages of these methods are and then propose a new method using fuzzy logic. Comparisons of results show that our method beats most methods which use machine learning as their core.
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paper_title: Lexrank: graph-based centrality as salience in text summarization

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We introduce a stochastic graph-based method for computing relative importance of textual units for Natural Language Processing. We test the technique on the problem of Text Summarization (TS). Extractive TS relies on the concept of sentence salience to identify the most important sentences in a document or set of documents. Salience is typically defined in terms of the presence of particular important words or in terms of similarity to a centroid pseudo-sentence. We consider a new approach, LexRank, for computing sentence importance based on the concept of eigenvector centrality in a graph representation of sentences. In this model, a connectivity matrix based on intra-sentence cosine similarity is used as the adjacency matrix of the graph representation of sentences. Our system, based on LexRank ranked in first place in more than one task in the recent DUC 2004 evaluation. In this paper we present a detailed analysis of our approach and apply it to a larger data set including data from earlier DUC evaluations. We discuss several methods to compute centrality using the similarity graph. The results show that degree-based methods (including LexRank) outperform both centroid-based methods and other systems participating in DUC in most of the cases. Furthermore, the LexRank with threshold method outperforms the other degree-based techniques including continuous LexRank. We also show that our approach is quite insensitive to the noise in the data that may result from an imperfect topical clustering of documents.
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paper_title: Automated Summarization Evaluation with Basic Elements

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As part of evaluating a summary automatically, it is usual to determine how much of the contents of one or more human-produced ‘ideal’ summaries it contains. Past automated methods such as ROUGE compare using fixed word ngrams, which are not ideal for a variety of reasons. In this paper we describe a framework in which summary evaluation measures can be instantiated and compared, and we implement a specific evaluation method using very small units of content, called Basic Elements, that address some of the shortcomings of ngrams. This method is tested on DUC 2003, 2004, and 2005 systems and produces very good correlations with human judgments.
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paper_title: A trainable document summarizer

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●● ● ● ● 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.
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paper_title: The automatic creation of literature abstracts

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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."
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paper_title: Document and passage retrieval based on hidden Markov models

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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.
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paper_title: New Methods in Automatic Extracting

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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.
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paper_title: Word Sequence Models for Single Text Summarization

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The main problem for generating an extractive automatic text summary is to detect the most relevant information in the source document. For such purpose, recently some approaches have successfully employed the word sequence information from the self-text for detecting the candidate text fragments for composing the summary. In this paper, we employ the so-called n-grams and maximal frequent word sequences as features in a vector space model in order to determine the advantages and disadvantages for extractive text summarization.
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paper_title: Optimizing Text Summarization Based on Fuzzy Logic

paper_content:

In this paper we first analyze some state of the art methods for text summarization. We discuss what the main disadvantages of these methods are and then propose a new method using fuzzy logic. Comparisons of results show that our method beats most methods which use machine learning as their core.
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paper_title: Narrative text classification for automatic key phrase extraction in web document corpora

paper_content:

Automatic key phrase extraction is a useful tool in many text related applications such as clustering and summarization. State-of-the-art methods are aimed towards extracting key phrases from traditional text such as technical papers. Application of these methods on Web documents, which often contain diverse and heterogeneous contents, is of particular interest and challenge in the information age. In this work, we investigate the significance of narrative text classification in the task of automatic key phrase extraction in Web document corpora. We benchmark three methods, TFIDF, KEA, and Keyterm, used to extract key phrases from all the plain text and from only the narrative text of Web pages. ANOVA tests are used to analyze the ranking data collected in a user study using quantitative measures of acceptable percentage and quality value. The evaluation shows that key phrases extracted from the narrative text only are significantly better than those obtained from all plain text of Web pages. This demonstrates that narrative text classification is indispensable for effective key phrase extraction in Web document corpora.
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paper_title: Generic text summarization using local and global properties of sentences

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With the proliferation of text data on the World-Wide Web, the development of methods for automatically summarizing these data becomes more important. Here, we propose a practical approach for extracting the most relevant sentences from the original document to form a summary. The idea of our approach is to exploit both the local and global properties of sentences. The local property can be considered as clusters of significant words within each sentence, while the global property can be though of as relations of all sentences in the document. These two properties are combined to get a single measure reflecting the informativeness of sentences. Experimental results show that our approach compares favorably to a commercial text summarizer.
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paper_title: Automatic Text Summarization using a Machine Learning Approach

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In this paper we address the automatic summarization task. Recent research works on extractive-summary generation employ some heuristics, but few works indicate how to select the relevant features. We will present a summarization procedure based on the application of trainable Machine Learning algorithms which employs a set of features extracted directly from the original text. These features are of two kinds: statistical - based on the frequency of some elements in the text; and linguistic - extracted from a simplified argumentative structure of the text. We also present some computational results obtained with the application of our summarizer to some well known text databases, and we compare these results to some baseline summarization procedures.
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paper_title: Automatic text summarization with neural networks

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A novel technique for summarizing news articles using neural networks is presented. A neural network is trained to learn the relevant features of sentences that should be included in the summary of the article. The neural network is then modified to generalize and combine the relevant features apparent in summary sentences. Finally, the modified neural network is used as a filter to summarize news articles.
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paper_title: Text Summarization Using Neural Networks

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Originally published in WSEAS Transactions on Systems, Volume 3, Issue 2, April 2004 (pp ::: 960-963).
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paper_title: Sentence Features Fusion for Text Summarization Using Fuzzy Logic

paper_content:

The scoring mechanism of the text features is the unique way for determining the key ideas in the text to be presented as text summary. The efficiency of the technique used for scoring the text sentences could produce good summary. The feature scores are imprecise and uncertain, this marks the differentiation between the important features and unimportant is difficult task. In this paper, we introduce fuzzy logic to deal with this problem. Our approach used important features based on fuzzy logic to extract the sentences. In our experiment, we used 30 test documents in DUC2002 data set. Each document is prepared by preprocessing process: sentence segmentation, tokenization, removing stop word, and word stemming. Then, we use 9 important features and calculate their score for each sentence. We propose a method using fuzzy logic for sentence extraction and compare our results with the baseline summarizer and Microsoft Word 2007 summarizers. The results show that the highest average precision, recall, and F-measure for the summaries were obtained from fuzzy method.
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paper_title: A cue-based hub-authority approach for multi-document text summarization

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Multi-document extractive summarization relies on the concept of sentence centrality to identify the most important sentences in a document. Although some research has introduced the graph-based ranking algorithms such as PageRank and HITS into the text summarization, we propose a new approach under the hub-authority framework in this paper. Our approach combines the text content with some cues such as "cue phrase", "sentence length" and "first sentence" and explores the sub-topics in the multi-documents by bringing the features of these sub-topics into graph-based sentence ranking algorithms. We provide an evaluation of our method on DUC 2004 data. The results show that our approach is an effective graph-ranking schema in multi-document generic text summarization.
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paper_title: Structure-based query-specific document summarization

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Summarization of text documents is increasingly important with the amount of data available on the Internet. The large majority of current approaches view documents as linear sequences of words and create query-independent summaries. However, ignoring the structure of the document degrades the quality of summaries. Furthermore, the popularity of web search engines requires query-specific summaries. We present a method to create query-specific summaries by adding structure to documents by extracting associations between their fragments.
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paper_title: MEAD - A Platform for Multidocument Multilingual Text Summarization

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Abstract This paper describes the functionality of MEAD, a comprehensive, public domain, open source, multidocument multilingual summarization environment that has been thus far downloaded by more than 500 organizations. MEAD has been used in a variety of summarization applications ranging from summarization for mobile devices to Web page summarization within a search engine and to novelty detection.
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Title: A Survey of Text Summarization Extractive Techniques

Section 1: INTRODUCTION
Description 1: This section introduces the importance of text summarization, differentiates between extractive and abstractive summarization methods, and explains the focus of the paper on extractive summarization methods.

Section 2: TEXT SUMMARIZATION EARLY HISTORY
Description 2: This section provides a historical overview of automatic text summarization, highlighting notable methods and systems from the 1950s to the 1990s.

Section 3: VARIOUS FEATURES FOR SUMMARIZATION
Description 3: This section discusses different features used in extractive summarization such as title words, sentence location, sentence length, proper nouns, upper-case words, cue phrases, and more.

Section 4: EXTRACTIVE SUMMARIZATION METHODS
Description 4: This section details various extractive summarization methods including TF-IDF, cluster-based methods, graph-theoretic approaches, machine learning approaches, LSA method, concept-obtained text summarization, neural networks, fuzzy logic, regression for estimating feature weights, multi-document extractive summarization, query-based extractive summarization, and multilingual extractive text summarization.

Section 5: CONCLUSIONS
Description 5: This section summarizes the insights gained from the survey, indicating the challenges, importance of feature weight determination, the need for NLP for coherence, and the evaluation methods for text summarization.