Text Summarization Techniques: A Brief Survey <s> Latent Semantic Analysis <s> 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. <s> BIB001 </s> Text Summarization Techniques: A Brief Survey <s> Latent Semantic Analysis <s> In this paper, we propose two generic text summarization methods that create text summaries by ranking and extracting sentences from the original documents. The first method uses standard IR methods to rank sentence relevances, while the second method uses the latent semantic analysis technique to identify semantically important sentences, for summary creations. Both methods strive to select sentences that are highly ranked and different from each other. This is an attempt to create a summary with a wider coverage of the document's main content and less redundancy. Performance evaluations on the two summarization methods are conducted by comparing their summarization outputs with the manual summaries generated by three independent human evaluators. The evaluations also study the influence of different VSM weighting schemes on the text summarization performances. Finally, the causes of the large disparities in the evaluators' manual summarization results are investigated, and discussions on human text summarization patterns are presented. <s> BIB002 </s> Text Summarization Techniques: A Brief Survey <s> Latent Semantic Analysis <s> A key task in an extraction system for query-oriented multi-document summarisation, necessary for computing relevance and redundancy, is modelling text semantics. In the Embra system, we use a representation derived from the singular value decomposition of a term co-occurrence matrix. We present methods to show the reliability of performance improvements. We find that Embra performs better with dimensionality reduction. <s> BIB003 </s> Text Summarization Techniques: A Brief Survey <s> Latent Semantic Analysis <s> We propose a new method for using anaphoric information in Latent Semantic Analysis (LSA), and discuss its application to develop an LSA-based summarizer which achieves a significantly better performance than a system not using anaphoric information, and a better performance by the rouge measure than all but one of the single-document summarizers participating in DUC-2002. Anaphoric information is automatically extracted using a new release of our own anaphora resolution system, GUITAR, which incorporates proper noun resolution. Our summarizer also includes a new approach for automatically identifying the dimensionality reduction of a document on the basis of the desired summarization percentage. Anaphoric information is also used to check the coherence of the summary produced by our summarizer, by a reference checker module which identifies anaphoric resolution errors caused by sentence extraction. <s> BIB004 </s> Text Summarization Techniques: A Brief Survey <s> Latent Semantic Analysis <s> Text summarization solves the problem of extracting important information from huge amount of text data. There are various methods in the literature that aim to find out well-formed summaries. One of the most commonly used methods is the Latent Semantic Analysis (LSA). In this paper, different LSA based summarization algorithms are explained and two new LSA based summarization algorithms are proposed. The algorithms are evaluated on Turkish documents, and their performances are compared using their ROUGE-L scores. One of our algorithms produces the best scores. <s> BIB005
Latent semantic analysis (LSA) which is introduced by BIB001 , is an unsupervised method for extracting a representation of text semantics based on observed words. Gong and Liu BIB002 initially proposed a method using LSA to select highly ranked sentences for single and multi-document summarization in the news domain. The LSA method first builds a term-sentence matrix (n by m matrix), where each row corresponds to a word from the input (n words) and each column corresponds to a sentence (m sentences). Each entry a i j of the matrix is the weight of the word i in sentence j. The weights of the words are computed by TFIDF technique and if a sentence does not have a word the weight of that word in the sentence is zero. Then singular value decomposition (SVD) is used on the matrix and transforms the matrix A into three matrices: A = U ΣV T . Matrix U (n ×m) represents a term-topic matrix having weights of words. Matrix Σ is a diagonal matrix (m × m) where each row i corresponds to the weight of a topic i. Matrix V T is the topicsentence matrix. The matrix D = ΣV T describes how much a sentence represent a topic, thus, d i j shows the weight of the topic i in sentence j. Gong and Liu's method was to choose one sentence per each topic, therefore, based on the length of summary in terms of sentences, they retained the number of topics. This strategy has a drawback due to the fact that a topic may need more than one sentence to convey its information. Consequently, alternative solutions were proposed to improve the performance of LSA-based techniques for summarization. One enhancement was to leverage the weight of each topic to decide the relative size of the summary that should cover the topic, which gives the flexibility of having a variable number of sentences. Another advancement is described in BIB004 . Steinberger et al. BIB004 introduced a LSA-based method which achieves a significantly better performance than the original work. They realized that the sentences that discuss some of important topics are good candidates for summaries, thus, in order to locate those sentences they defined the weight of the sentence as follows: Let be the "weight" function, then For other variations of LSA technique, see BIB003 BIB005 .