Text Summarization Techniques: A Brief Survey <s> Topic Words <s> 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." <s> BIB001 </s> Text Summarization Techniques: A Brief Survey <s> Topic Words <s> Much work has been done on the statistical analysis of text. In some cases reported in the literature, inappropriate statistical methods have been used, and statistical significance of results have not been addressed. In particular, asymptotic normality assumptions have often been used unjustifiably, leading to flawed results.This assumption of normal distribution limits the ability to analyze rare events. Unfortunately rare events do make up a large fraction of real text.However, more applicable methods based on likelihood ratio tests are available that yield good results with relatively small samples. These tests can be implemented efficiently, and have been used for the detection of composite terms and for the determination of domain-specific terms. In some cases, these measures perform much better than the methods previously used. In cases where traditional contingency table methods work well, the likelihood ratio tests described here are nearly identical.This paper describes the basis of a measure based on likelihood ratios that can be applied to the analysis of text. <s> BIB002 </s> Text Summarization Techniques: A Brief Survey <s> Topic Words <s> The problem of using topic representations for multi-document summarization (MDS) has received considerable attention recently. In this paper, we describe five different topic representations and introduce a novel representation of topics based on topic themes. We present eight different methods of generating MDS and evaluate each of these methods on a large set of topics used in past DUC workshops. Our evaluation results show a significant improvement in the quality of summaries based on topic themes over MDS methods that use other alternative topic representations. <s> BIB003 </s> Text Summarization Techniques: A Brief Survey <s> Topic Words <s> Numerous approaches for identifying important content for automatic text summarization have been developed to date. Topic representation approaches first derive an intermediate representation of the text that captures the topics discussed in the input. Based on these representations of topics, sentences in the input document are scored for importance. In contrast, in indicator representation approaches, the text is represented by a diverse set of possible indicators of importance which do not aim at discovering topicality. These indicators are combined, very often using machine learning techniques, to score the importance of each sentence. Finally, a summary is produced by selecting sentences in a greedy approach, choosing the sentences that will go in the summary one by one, or globally optimizing the selection, choosing the best set of sentences to form a summary. In this chapter we give a broad overview of existing approaches based on these distinctions, with particular attention on how representation, sentence scoring or summary selection strategies alter the overall performance of the summarizer. We also point out some of the peculiarities of the task of summarization which have posed challenges to machine learning approaches for the problem, and some of the suggested solutions. <s> BIB004
The topic words technique is one of the common topic representation approaches which aims to identify words that describe the topic of the input document. BIB001 was one the earliest works that leveraged this method by using frequency thresholds to locate the descriptive words in the document and represent the topic of the document. A more advanced version of Luhn's idea was presented in BIB002 in which they used log-likelihood ratio test to identify explanatory words which in summarization literature are called the "topic signature". Utilizing topic signature words as topic representation was very effective and increased the accuracy of multidocument summarization in the news domain BIB003 . For more information about log-likelihood ratio test, see BIB004 . There are two ways to compute the importance of a sentence: as a function of the number of topic signatures it contains, or as the proportion of the topic signatures in the sentence. Both sentence scoring functions relate to the same topic representation, however, they might assign different scores to sentences. The first method may assign higher scores to longer sentences, because they have more words. The second approach measures the density of the topic words.