Text Summarization Techniques: A Brief Survey <s> TFIDF. <s> We present a multi-document summarizer, MEAD, which generates summaries using cluster centroids produced by a topic detection and tracking system. We describe two new techniques, a centroid-based summarizer, and an evaluation scheme based on sentence utility and subsumption. We have applied this evaluation to both single and multiple document summaries. Finally, we describe two user studies that test our models of multi-document summarization. <s> BIB001 </s> Text Summarization Techniques: A Brief Survey <s> TFIDF. <s> The Markov Random Walk model has been recently exploited for multi-document summarization by making use of the link relationships between sentences in the document set, under the assumption that all the sentences are indistinguishable from each other. However, a given document set usually covers a few topic themes with each theme represented by a cluster of sentences. The topic themes are usually not equally important and the sentences in an important theme cluster are deemed more salient than the sentences in a trivial theme cluster. This paper proposes the Cluster-based Conditional Markov Random Walk Model (ClusterCMRW) and the Cluster-based HITS Model (ClusterHITS) to fully leverage the cluster-level information. Experimental results on the DUC2001 and DUC2002 datasets demonstrate the good effectiveness of our proposed summarization models. The results also demonstrate that the ClusterCMRW model is more robust than the ClusterHITS model, with respect to different cluster numbers. <s> BIB002 </s> Text Summarization Techniques: A Brief Survey <s> TFIDF. <s> In paper, we propose an unsupervised text summarization model which generates a summary by extracting salient sentences in given document(s). In particular, we model text summarization as an integer linear programming problem. One of the advantages of this model is that it can directly discover key sentences in the given document(s) and cover the main content of the original document(s). This model also guarantees that in the summary can not be multiple sentences that convey the same information. The proposed model is quite general and can also be used for single- and multi-document summarization. We implemented our model on multi-document summarization task. Experimental results on DUC2005 and DUC2007 datasets showed that our proposed approach outperforms the baseline systems. <s> BIB003 </s> Text Summarization Techniques: A Brief Survey <s> TFIDF. <s> 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. <s> BIB004 </s> Text Summarization Techniques: A Brief Survey <s> TFIDF. <s> This paper proposes an optimization-based model for generic document summarization. The model generates a summary by extracting salient sentences from documents. This approach uses the sentence-to-document collection, the summary-to-document collection and the sentence-to-sentence relations to select salient sentences from given document collection and reduce redundancy in the summary. To solve the optimization problem has been created an improved differential evolution algorithm. The algorithm can adjust crossover rate adaptively according to the fitness of individuals. We implemented the proposed model on multi-document summarization task. Experiments have been performed on DUC2002 and DUC2004 data sets. The experimental results provide strong evidence that the proposed optimization-based approach is a viable method for document summarization. <s> BIB005
Since word probability techniques depend on a stop word list in order to not consider them in the summary and because deciding which words to put in the stop list is not very straight forward, there is a need for more advanced techniques. One of the more advanced and very typical methods to give weight to words is TFIDF (Term Frequency Inverse Document Frequency). This weighting technique assesses the importance of words and identifies very common words (that should be omitted from consideration) in the document(s) by giving low weights to words appearing in most documents. The weight of each word w in document d is computed as follows: where f d (w) is term frequency of word w in the document d, f D (w) is the number of documents that contain word w and |D| is the number of documents in the collection D. For more information about TFIDF and other term weighting schemes, see  . TFIDF weights are easy and fast to compute and also are good measures for determining the importance of sentences, therefore many existing summarizers BIB003 BIB005 BIB004 have utilized this technique (or some form of it). Centroid-based summarization, another set of techniques which has become a common baseline, is based on TFIDF topic representation. This kind of method ranks sentences by computing their salience using a set of features. A complete overview of the centroidbased approach is available in BIB001 but we outline briefly the basic idea. The first step is topic detection and documents that describe the same topic clustered together. To achieve this goal, TFIDF vector representations of the documents are created and those words whose TFIDF scores are below a threshold are removed. Then, a clustering algorithm is run over the TFIDF vectors, consecutively adding documents to clusters and recomputing the centroids according to: where c j is the centroid of the jth cluster and C j is the set of documents that belong to that cluster. Centroids can be considered as pseudo-documents that consist of those words whose TFIDF scores are higher than the threshold and form the cluster. The second step is using centroids to identify sentences in each cluster that are central to topic of the entire cluster. To accomplish this goal, two metrics are defined  : cluster-based relative utility (CBRU) and cross-sentence informational subsumption (CSIS). CBRU decides how relevant a particular sentence is to the general topic of the entire cluster and CSIS measure redundancy among sentences. In order to approximate two metrics, three features (i.e. central value, positional value and first-sentence overlap) are used. Next, the final score of each sentence is computed and the selection of sentences is determined. For another related work, see BIB002 .