Text Summarization Techniques: A Brief Survey <s> Machine Learning for Summarization <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> BIB001 </s> Text Summarization Techniques: A Brief Survey <s> Machine Learning for Summarization <s> A sentence extract summary of a document is a subset of the document's sentences that contains the main ideas in the document. We present an approach to generating such summaries, a hidden Markov model that judges the likelihood that each sentence should be contained in the summary. We compare the results of this method with summaries generated by humans, showing that we obtain significantly higher agreement than do earlier methods. <s> BIB002 </s> Text Summarization Techniques: A Brief Survey <s> Machine Learning for Summarization <s> We describe a biographical multi-document summarizer that summarizes information about people described in the news. The summarizer uses corpus statistics along with linguistic knowledge to select and merge descriptions of people from a document collection, removing redundant descriptions. The summarization components have been extensively evaluated for coherence, accuracy, and non-redundancy of the descriptions produced. <s> BIB003 </s> Text Summarization Techniques: A Brief Survey <s> Machine Learning for Summarization <s> In this article we propose a strategy for the summarization of scientific articles that concentrates on the rhetorical status of statements in an article: Material for summaries is selected in such a way that summaries can highlight the new contribution of the source article and situate it with respect to earlier work.We provide a gold standard for summaries of this kind consisting of a substantial corpus of conference articles in computational linguistics annotated with human judgments of the rhetorical status and relevance of each sentence in the articles. We present several experiments measuring our judges' agreement on these annotations.We also present an algorithm that, on the basis of the annotated training material, selects content from unseen articles and classifies it into a fixed set of seven rhetorical categories. The output of this extraction and classification system can be viewed as a single-document summary in its own right; alternatively, it provides starting material for the generation of task-oriented and user-tailored summaries designed to give users an overview of a scientific field. <s> BIB004 </s> Text Summarization Techniques: A Brief Survey <s> Machine Learning for Summarization <s> A serious bottleneck in the development of trainable text summarization systems is the shortage of training data. Constructing such data is a very tedious task, especially because there are in general many different correct ways to summarize a text. Fortunately we can utilize the Internet as a source of suitable training data. In this paper, we present a summarization system that uses the web as the source of training data. The procedure involves structuring the articles downloaded from various websites, building adequate corpora of (summary, text) and (extract, text) pairs, training on positive and negative data, and automatically learning to perform the task of extraction-based summarization at a level comparable to the best DUC systems. <s> BIB005 </s> Text Summarization Techniques: A Brief Survey <s> Machine Learning for Summarization <s> In this paper we describe a biography summarization system using sentence classification and ideas from information retrieval. Although the individual techniques are not new, assembling and applying them to generate multi-document biographies is new. Our system was evaluated in DUC2004. It is among the top performers in task 5–short summaries focused by person questions. <s> BIB006 </s> Text Summarization Techniques: A Brief Survey <s> Machine Learning for Summarization <s> It is difficult to identify sentence importance from a single point of view. In this paper, we propose a learning-based approach to combine various sentence features. They are categorized as surface, content, relevance and event features. Surface features are related to extrinsic aspects of a sentence. Content features measure a sentence based on content-conveying words. Event features represent sentences by events they contained. Relevance features evaluate a sentence from its relatedness with other sentences. Experiments show that the combined features improved summarization performance significantly. Although the evaluation results are encouraging, supervised learning approach requires much labeled data. Therefore we investigate co-training by combining labeled and unlabeled data. Experiments show that this semi-supervised learning approach achieves comparable performance to its supervised counterpart and saves about half of the labeling time cost. <s> BIB007 </s> Text Summarization Techniques: A Brief Survey <s> Machine Learning for Summarization <s> Quickly moving to a new area of research is painful for researchers due to the vast amount of scientific literature in each field of study. One possible way to overcome this problem is to summarize a scientific topic. In this paper, we propose a model of summarizing a single article, which can be further used to summarize an entire topic. Our model is based on analyzing others' viewpoint of the target article's contributions and the study of its citation summary network using a clustering approach. <s> BIB008 </s> Text Summarization Techniques: A Brief Survey <s> Machine Learning for Summarization <s> Most existing research on applying machine learning techniques to document summarization explores either classification models or learning-to-rank models. This paper presents our recent study on how to apply a different kind of learning models, namely regression models, to query-focused multi-document summarization. We choose to use Support Vector Regression (SVR) to estimate the importance of a sentence in a document set to be summarized through a set of pre-defined features. In order to learn the regression models, we propose several methods to construct the ''pseudo'' training data by assigning each sentence with a ''nearly true'' importance score calculated with the human summaries that have been provided for the corresponding document set. A series of evaluations on the DUC data sets are conducted to examine the efficiency and the robustness of the proposed approaches. When compared with classification models and ranking models, regression models are consistently preferable. <s> BIB009 </s> Text Summarization Techniques: A Brief Survey <s> Machine Learning for Summarization <s> Researchers and scientists increasingly find themselves in the position of having to quickly understand large amounts of technical material. Our goal is to effectively serve this need by using bibliometric text mining and summarization techniques to generate summaries of scientific literature. We show how we can use citations to produce automatically generated, readily consumable, technical extractive summaries. We first propose C-LexRank, a model for summarizing single scientific articles based on citations, which employs community detection and extracts salient information-rich sentences. Next, we further extend our experiments to summarize a set of papers, which cover the same scienti fic topic. We generate extractive summaries of a set of Question Answering (QA) and Dependency Parsing (DP) papers, their abstracts, and their citation sentences and show that citations have unique information amenable to creating a summary. <s> BIB010
Machine learning approaches model the summarization as a classification problem. BIB001 is an early research attempt at applying machine learning techniques for summarization. Kupiec et al. develop a classification function, naive-Bayes classifier, to classify the sentences as summary sentences and non-summary sentences based on the features they have, given a training set of documents and their extractive summaries. The classification probabilities are learned statistically from the training data using Bayes' rule: where s is a sentence from the document collection, F 1 , F 2 , . . . , F k are features used in classification and S is the summary to be generated. Assuming the conditional independence between the features: The probability a sentence to belongs to the summary is the score of the sentence. The selected classifier plays the role of a sentence scoring function. Some of the frequent features used in summarization include the position of sentences in the document, sentence length, presence of uppercase words, similarity of the sentence to the document title, etc. Machine learning approaches have been widely used in summarization by BIB009 BIB007 BIB005 , to name a few. Naive Bayes, decision trees, support vector machines, Hidden Markov models and Conditional Random Fields are among the most common machine learning techniques used for summarization. One fundamental difference between classifiers is that sentences to be included in the summary have to be decided independently. It turns out that methods explicitly assuming the dependency between sentences such as Hidden Markov model BIB002 and Conditional Random Fields  often outperform other techniques. One of the primary issues in utilizing supervised learning methods for summarization is that they need a set of training documents (labeled data) to train the classifier, which may not be always easily available. Researchers have proposed some alternatives to cope with this issue: • Annotated corpora creation: Creating annotated corpus for summarization greatly benefits the researchers, because more public benchmarks will be available which makes it easier to compare different summarization approaches together. It also lowers the risk of overfitting with a limited data. Ulrich et al.  introduce a publicly available annotated email corpus and its creation process. However, creating annotated corpus is very time consuming and more critically, there is no standard agreement on choosing the sentences, and different people may select varied sentences to construct the summary. • Semi-supervised approaches: Using a semi-supervised technique to train a classifier. In semi-supervised learning we utilize the unlabeled data in training. There is usually a small amount of labeled data along with a large amount of unlabeled data. For complete overview of semisupervised learning, see  . Wong et al. BIB007 proposed a semi-supervised method for extractive summarization. They co-trained two classifiers iteratively to exploit unlabeled data. In each iteration, the unlabeled training examples (sentences) with top scores are included in the labeled training set, and the two classifiers are trained on the new training data. Machine learning methods have been shown to be very effective and successful in single and multi-document summarization, specifically in class specific summarization where classifiers are trained to locate particular type of information such as scientific paper summarization BIB008 BIB010 BIB004 and biographical summaries BIB003  BIB006 .