Text Summarization Techniques: A Brief Survey <s> Web Summarization <s> This paper addresses the issue of Web document summarization. As textual content of Web documents is often scarce or irrelevant and existing summarization techniques are based on it, many Web pages and websites cannot be suitably summarized. We consider the context of a Web document by the textual content of all the documents linking to it. To summarize a target Web document, a context-based summarizer has to perform a preprocessing task, during which it will be decided which pieces of information in the source documents are relevant to the content of the target. Then a context-based summarizer faces two issues: first, the selected elements may partially deal with the topic of the target, second they may be related to the target and yet not contain any clues about the content of the target.In this paper we put forward two new summarization by context algorithms. The first one uses both the content and the context of the document and the second one is based only on the elements of the context. It is shown that summaries taking into account the context are usually much more relevant than those made only from the content of the target document. Optimal conditions of the proposed algorithms with respect to the sizes of the content and the context of the document to summarize are studied. <s> BIB001 </s> Text Summarization Techniques: A Brief Survey <s> Web Summarization <s> Much existing research on blogs focused on posts only, ignoring their comments. Our user study conducted on summarizing blog posts, however, showed that reading comments does change one's understanding about blog posts. In this research, we aim to extract representative sentences from a blog post that best represent the topics discussed among its comments. The proposed solution first derives representative words from comments and then selects sentences containing representative words. The representativeness of words is measured using ReQuT (i.e., Reader, Quotation, and Topic). Evaluated on human labeled sentences, ReQuT together with summation-based sentence selection showed promising results. <s> BIB002 </s> Text Summarization Techniques: A Brief Survey <s> Web Summarization <s> Comments left by readers on Web documents contain valuable information that can be utilized in different information retrieval tasks including document search, visualization, and summarization. In this paper, we study the problem of comments-oriented document summarization and aim to summarize a Web document (e.g., a blog post) by considering not only its content, but also the comments left by its readers. We identify three relations (namely, topic, quotation, and mention) by which comments can be linked to one another, and model the relations in three graphs. The importance of each comment is then scored by: (i) graph-based method, where the three graphs are merged into a multi-relation graph; (ii) tensor-based method, where the three graphs are used to construct a 3rd-order tensor. To generate a comments-oriented summary, we extract sentences from the given Web document using either feature-biased approach or uniform-document approach. The former scores sentences to bias keywords derived from comments; while the latter scores sentences uniformly with comments. In our experiments using a set of blog posts with manually labeled sentences, our proposed summarization methods utilizing comments showed significant improvement over those not using comments. The methods using feature-biased sentence extraction approach were observed to outperform that using uniform-document approach. <s> BIB003 </s> Text Summarization Techniques: A Brief Survey <s> Web Summarization <s> In this paper, we focus on a recent Web trend called microblogging, and in particular a site called Twitter. The content of such a site is an extraordinarily large number of small textual messages, posted by millions of users, at random or in response to perceived events or situations. We have developed an algorithm that takes a trending phrase or any phrase specified by a user, collects a large number of posts containing the phrase, and provides an automatically created summary of the posts related to the term. We present examples of summaries we produce along with initial evaluation. <s> BIB004 </s> Text Summarization Techniques: A Brief Survey <s> Web Summarization <s> Owing to the sheer volume of text generated by a microblog site like Twitter, it is often difficult to fully understand what is being said about various topics. This paper presents algorithms for summarizing microblog documents. Initially, we present algorithms that produce single-document summaries but later extend them to produce summaries containing multiple documents. We evaluate the generated summaries by comparing them to both manually produced summaries and, for the multiple-post summaries, to the summarization results of some of the leading traditional summarization systems. <s> BIB005
Web pages contains lots of elements which cannot be summarized such as pictures. The textual information they have is often scarce, which makes applying text summarization techniques limited. Nonetheless, we can consider the context of a web page, i.e. pieces of information extracted from content of all the pages linking to it, as additional material to improve summarization. The earliest research in this regard is  where they query web search engines and fetch the pages having links to the specified web page. Then they analyze the candidate pages and select the best sentences containing links to the web page heuristically. Delort et al. BIB001 extended and improved this approach by using an algorithm trying to select a sentence about the same topic that covers as many aspects of the web page as possible. For blog summarization, BIB002 proposed a method that first derives representative words from comments and then selects important sentences from the blog post containing representative words. For more related works, see BIB003 BIB004 BIB005 .