Text Summarization Techniques: A Brief Survey <s> Frequency-driven Approaches <s> In recent years, there has been increased interest in topic-focused multi-document summarization. In this task, automatic summaries are produced in response to a specific information request, or topic, stated by the user. The system we have designed to accomplish this task comprises four main components: a generic extractive summarization system, a topic-focusing component, sentence simplification, and lexical expansion of topic words. This paper details each of these components, together with experiments designed to quantify their individual contributions. We include an analysis of our results on two large datasets commonly used to evaluate task-focused summarization, the DUC2005 and DUC2006 datasets, using automatic metrics. Additionally, we include an analysis of our results on the DUC2006 task according to human evaluation metrics. In the human evaluation of system summaries compared to human summaries, i.e., the Pyramid method, our system ranked first out of 22 systems in terms of overall mean Pyramid score; and in the human evaluation of summary responsiveness to the topic, our system ranked third out of 35 systems. <s> BIB001 </s> Text Summarization Techniques: A Brief Survey <s> Frequency-driven Approaches <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> BIB002
When assigning weights of words in topic representations, we can think of binary (0 or 1) or real-value (continuous) weights and decide which words are more correlated to the topic. The two most common techniques in this category are: word probability and TFIDF (Term Frequency Inverse Document Frequency). 3.2.1 Word Probability. The simplest method to use frequency of words as indicators of importance is word probability. The probability of a word w is determined as the number of occurrences of the word, f (w), divided by the number of all words in the input (which can be a single document or multiple documents): Vanderwende et al. BIB001 proposed the SumBasic system which uses only the word probability approach to determine sentence importance. For each sentence, S j , in the input, it assigns a weight equal to the average probability of the words in the sentence: where (S j ) is the weight of sentence S j . In the next step, it picks the best scoring sentence that contains the highest probability word. This step ensures that the highest probability word, which represents the topic of the document at that point, is included in the summary. Then for each word in the chosen sentence, the weight is updated: This word weight update indicates that the probability of a word appearing in the summary is lower than a word occurring once. The aforementioned selection steps will repeat until the desired length summary is reached. The sentence selection approach used by SumBasic is based on the greedy strategy. Yih et al.  used an optimization approach (as sentence selection strategy) to maximize the occurrence of the important words globally over the entire summary. BIB002 is another example of using an optimization approach.