Recent automatic text summarization techniques: a survey
TL;DR: A comprehensive survey of recent text summarization extractive approaches developed in the last decade is presented and the discussion of useful future directions that can help researchers to identify areas where further research is needed are discussed.
Abstract: As information is available in abundance for every topic on internet, condensing the important information in the form of summary would benefit a number of users. Hence, there is growing interest among the research community for developing new approaches to automatically summarize the text. Automatic text summarization system generates a summary, i.e. short length text that includes all the important information of the document. Since the advent of text summarization in 1950s, researchers have been trying to improve techniques for generating summaries so that machine generated summary matches with the human made summary. Summary can be generated through extractive as well as abstractive methods. Abstractive methods are highly complex as they need extensive natural language processing. Therefore, research community is focusing more on extractive summaries, trying to achieve more coherent and meaningful summaries. During a decade, several extractive approaches have been developed for automatic summary generation that implements a number of machine learning and optimization techniques. This paper presents a comprehensive survey of recent text summarization extractive approaches developed in the last decade. Their needs are identified and their advantages and disadvantages are listed in a comparative manner. A few abstractive and multilingual text summarization approaches are also covered. Summary evaluation is another challenging issue in this research field. Therefore, intrinsic as well as extrinsic both the methods of summary evaluation are described in detail along with text summarization evaluation conferences and workshops. Furthermore, evaluation results of extractive summarization approaches are presented on some shared DUC datasets. Finally this paper concludes with the discussion of useful future directions that can help researchers to identify areas where further research is needed.
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TL;DR: This research provides a comprehensive survey for the researchers by presenting the different aspects of ATS: approaches, methods, building blocks, techniques, datasets, evaluation methods, and future research directions.
Abstract: Automatic Text Summarization (ATS) is becoming much more important because of the huge amount of textual content that grows exponentially on the Internet and the various archives of news articles, scientific papers, legal documents, etc. Manual text summarization consumes a lot of time, effort, cost, and even becomes impractical with the gigantic amount of textual content. Researchers have been trying to improve ATS techniques since the 1950s. ATS approaches are either extractive, abstractive, or hybrid. The extractive approach selects the most important sentences in the input document(s) then concatenates them to form the summary. The abstractive approach represents the input document(s) in an intermediate representation then generates the summary with sentences that are different than the original sentences. The hybrid approach combines both the extractive and abstractive approaches. Despite all the proposed methods, the generated summaries are still far away from the human-generated summaries. Most researches focus on the extractive approach. It is required to focus more on the abstractive and hybrid approaches. This research provides a comprehensive survey for the researchers by presenting the different aspects of ATS: approaches, methods, building blocks, techniques, datasets, evaluation methods, and future research directions.
324 citations
TL;DR: This study proposes a novel multi-text summarization technique for identifying the top-k most informative sentences of hotel reviews, and developed a new sentence importance metric.
Abstract: Text summarization technique can extract essential information from online reviews.Our method can identify top-k most informative sentences from online hotel reviews.We jointly considered author, review time, usefulness, and opinion factors.Online hotel reviews were collected from TripAdvisor in experimental evaluation.The results show that our approach provides more comprehensive hotel information. Online travel forums and social networks have become the most popular platform for sharing travel information, with enormous numbers of reviews posted daily. Automatically generated hotel summaries could aid travelers in selecting hotels. This study proposes a novel multi-text summarization technique for identifying the top-k most informative sentences of hotel reviews. Previous studies on review summarization have primarily examined content analysis, which disregards critical factors like author credibility and conflicting opinions. We considered such factors and developed a new sentence importance metric. Both the content and sentiment similarities were used to determine the similarity of two sentences. To identify the top-k sentences, the k-medoids clustering algorithm was used to partition sentences into k groups. The medoids from these groups were then selected as the final summarization results. To evaluate the performance of the proposed method, we collected two sets of reviews for the two hotels posted on TripAdvisor.com. A total of 20 subjects were invited to review the text summarization results from the proposed approach and two conventional approaches for the two hotels. The results indicate that the proposed approach outperforms the other two, and most of the subjects believed that the proposed approach can provide more comprehensive hotel information.
243 citations
TL;DR: Significant contributions made in recent years are emphasized, including progress on modern sentence extraction approaches that improve concept coverage, information diversity and content coherence, as well as attempts from summarization frameworks that integrate sentence compression, and more abstractive systems that are able to produce completely new sentences.
Abstract: The task of automatic document summarization aims at generating short summaries for originally long documents. A good summary should cover the most important information of the original document or a cluster of documents, while being coherent, non-redundant and grammatically readable. Numerous approaches for automatic summarization have been developed to date. In this paper we give a self-contained, broad overview of recent progress made for document summarization within the last 5 years. Specifically, we emphasize on significant contributions made in recent years that represent the state-of-the-art of document summarization, including progress on modern sentence extraction approaches that improve concept coverage, information diversity and content coherence, as well as attempts from summarization frameworks that integrate sentence compression, and more abstractive systems that are able to produce completely new sentences. In addition, we review progress made for document summarization in domains, genres and applications that are different from traditional settings. We also point out some of the latest trends and highlight a few possible future directions.
143 citations
Cites background from "Recent automatic text summarization..."
...There exist very few similar attempts (such as [54]) that unfortunately fail to cover the most significant study trends or are in shortage of clear organization of content, which partly motivates us for writing this survey for recent studies....
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01 Jan 2009
TL;DR: A new unsupervised method using Non-negative Matrix Factorization (NMF) to select sentences for automatic generic document summarization, which uses non-negative constraints, which are more similar to the human cognition process.
Abstract: In existing unsupervised methods, Latent Semantic Analysis (LSA) is used for sentence selection. However, the obtained results are less meaningful, because singular vectors are used as the bases for sentence selection from given documents, and singular vector components can have negative values. We propose a new unsupervised method using Non-negative Matrix Factorization (NMF) to select sentences for automatic generic document summarization. The proposed method uses non-negative constraints, which are more similar to the human cognition process. As a result, the method selects more meaningful sentences for generic document summarization than those selected using LSA.
131 citations
Posted Content•
TL;DR: This article provides a comprehensive literature survey on different seq2seq models for abstractive text summarization from the viewpoint of network structures, training strategies, and summary generation algorithms.
Abstract: In the past few years, neural abstractive text summarization with sequence-to-sequence (seq2seq) models have gained a lot of popularity. Many interesting techniques have been proposed to improve seq2seq models, making them capable of handling different challenges, such as saliency, fluency and human readability, and generate high-quality summaries. Generally speaking, most of these techniques differ in one of these three categories: network structure, parameter inference, and decoding/generation. There are also other concerns, such as efficiency and parallelism for training a model. In this paper, we provide a comprehensive literature survey on different seq2seq models for abstractive text summarization from the viewpoint of network structures, training strategies, and summary generation algorithms. Several models were first proposed for language modeling and generation tasks, such as machine translation, and later applied to abstractive text summarization. Hence, we also provide a brief review of these models. As part of this survey, we also develop an open source library, namely, Neural Abstractive Text Summarizer (NATS) toolkit, for the abstractive text summarization. An extensive set of experiments have been conducted on the widely used CNN/Daily Mail dataset to examine the effectiveness of several different neural network components. Finally, we benchmark two models implemented in NATS on the two recently released datasets, namely, Newsroom and Bytecup.
105 citations
Cites methods from "Recent automatic text summarization..."
...A method is considered to be extractive if words, phrases, and sentences in the summaries are selected from the source articles [4, 5, 6, 2, 7, 8, 9, 10]....
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References
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TL;DR: In this article, a new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented, which requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.
Abstract: A new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented. By means of an extensive testbed it is demonstrated that the new method converges faster and with more certainty than many other acclaimed global optimization methods. The new method requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.
24,053 citations
01 Apr 1998
TL;DR: This paper provides an in-depth description of Google, a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext and looks at the problem of how to effectively deal with uncontrolled hypertext collections where anyone can publish anything they want.
Abstract: In this paper, we present Google, a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext. Google is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems. The prototype with a full text and hyperlink database of at least 24 million pages is available at http://google.stanford.edu/. To engineer a search engine is a challenging task. Search engines index tens to hundreds of millions of web pages involving a comparable number of distinct terms. They answer tens of millions of queries every day. Despite the importance of large-scale search engines on the web, very little academic research has been done on them. Furthermore, due to rapid advance in technology and web proliferation, creating a web search engine today is very different from three years ago. This paper provides an in-depth description of our large-scale web search engine -- the first such detailed public description we know of to date. Apart from the problems of scaling traditional search techniques to data of this magnitude, there are new technical challenges involved with using the additional information present in hypertext to produce better search results. This paper addresses this question of how to build a practical large-scale system which can exploit the additional information present in hypertext. Also we look at the problem of how to effectively deal with uncontrolled hypertext collections where anyone can publish anything they want.
14,696 citations
"Recent automatic text summarization..." refers methods in this paper
...The relevance of the graph nodes is estimated by a variant of the traditional PageRank (Brin and Page 1998) graph ranking algorithm....
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Journal Article•
TL;DR: Google as discussed by the authors is a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext and is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems.
13,327 citations
TL;DR: A new method for automatic indexing and retrieval to take advantage of implicit higher-order structure in the association of terms with documents (“semantic structure”) in order to improve the detection of relevant documents on the basis of terms found in queries.
Abstract: A new method for automatic indexing and retrieval is described. The approach is to take advantage of implicit higher-order structure in the association of terms with documents (“semantic structure”) in order to improve the detection of relevant documents on the basis of terms found in queries. The particular technique used is singular-value decomposition, in which a large term by document matrix is decomposed into a set of ca. 100 orthogonal factors from which the original matrix can be approximated by linear combination. Documents are represented by ca. 100 item vectors of factor weights. Queries are represented as pseudo-document vectors formed from weighted combinations of terms, and documents with supra-threshold cosine values are returned. initial tests find this completely automatic method for retrieval to be promising.
12,443 citations
"Recent automatic text summarization..." refers methods in this paper
...LSA+TRM approach uses LSA (Landauer et al. 1998; Deerwester et al. 1990) to obtain a document’s semantic matrix and builds a relationship map for semantic text by employing a sentence’s semantic representation....
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...The entire process of LSA+TRM approach shown above in Fig....
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...LSA+TRM approach uses LSA (Landauer et al. 1998; Deerwester et al. 1990) to obtain a document’s semantic matrix and builds a relationship map for semantic text by...
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...MCBA and LSA+TRM approach focus on summarizing single documents and produce indicative, extract based summaries....
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...LSA is used to extract latent structures from a document....
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Book•
01 Jan 2008TL;DR: In this article, the authors present an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections.
Abstract: Class-tested and coherent, this groundbreaking new textbook teaches web-era information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. Written from a computer science perspective by three leading experts in the field, it gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Although originally designed as the primary text for a graduate or advanced undergraduate course in information retrieval, the book will also create a buzz for researchers and professionals alike.
11,804 citations