A web assessment approach based on summarisation and visualisation

The number of Web sites has noticeably increased to roughly 224 million in last ten years. This means there is a rapid growth of information on the Internet. Although search engines can help users to filter their desired information, the searched result is normally presented in the form of a very long list, and users have to visit each Web page in order to determine the appropriateness of the result. This leads to a considerable amount of time has to be spent on finding the required information. To address this issue, this paper proposes a Web assessment approach in order to provide an overview of the information on a Website using an integration of existing summarisation and visualisation techniques, which are text summarisation, tag cloud, Document Type View, and interactive features. This approach is capable to reduce the time required to identify and search for information from the Web

Summarizing information from Web sites on distributed power generation and alternative energy development

The World Wide Web (WWW) has become a huge repository of information and knowledge, and an essential channel for information exchange. Many sites and thousands of pages of information on distributed power generation and alternate energy development are being added or modified constantly and the task of finding the most appropriate information is getting difficult. While search engines are capable to return a collection of links according to key terms and some forms of ranking mechanism, it is still necessary to access the Web page and navigate through the site in order to find the information. This paper proposes an interactive summarization framework called iWISE to facilitate the process by providing a summary of the information on the Web site. The proposed approach makes use of graphical visualization, tag clouds and text summarization. A number of cases are presented and compared in this paper with a discussion on future work

Text Summarization Techniques: A Brief Survey

In recent years, there has been a explosion in the amount of text data from a variety of sources. This volume of text is an invaluable source of information and knowledge which needs to be effectively summarized to be useful. In this review, the main approaches to automatic text summarization are described. We review the different processes for summarization and describe the effectiveness and shortcomings of the different methods.Comment: Some of references format have update

Do peers see more in a paper than its authors?

Recent years have shown a gradual shift in the content of biomedical publications that is freely accessible, from titles and abstracts to full text. This has enabled new forms of automatic text analysis and has given rise to some interesting questions: How informative is the abstract compared to the full-text? What important information in the full-text is not present in the abstract? What should a good summary contain that is not already in the abstract? Do authors and peers see an article differently? We answer these questions by comparing the information content of the abstract to that in citances-sentences containing citations to that article. We contrast the important points of an article as judged by its authors versus as seen by peers. Focusing on the area of molecular interactions, we perform manual and automatic analysis, and we find that the set of all citances to a target article not only covers most information (entities, functions, experimental methods, and other biological concepts) found in its abstract, but also contains 20% more concepts. We further present a detailed summary of the differences across information types, and we examine the effects other citations and time have on the content of citances

Integrating the document object model with hyperlinks for enhanced topic distillation and information extraction

Topic distillation is the process of finding authoritative Web pages and comprehensive “hubs” which reciprocally endorse each other and are relevant to a given query. Hyperlink-based topic distillation has been traditionally applied to a macroscopic Web model where documents are nodes in a directed graph and hyperlinks are edges. Macroscopic models miss valuable clues such as banners, navigation panels, and template-based inclusions, which are embedded in HTML pages using markup tags. Consequently, results of macroscopic distillation algorithms have been deteriorating in quality as Web pages are becoming more complex. We propose a uniform fine-grained model for the Web in which pages are represented by their tag trees (also called their Document Object Models or DOMs) and these DOM trees are interconnected by ordinary hyperlinks. Surprisingly, macroscopic distillation algorithms do not work in the finegrained scenario. We present a new algorithm suitable for the fine-grained model. It can dis-aggregate hubs into coherent regions by segmenting their DOM trees. Mutual endorsement between hubs and authorities involve these regions, rather than single nodes representing complete hubs. Anecdotes and measurements using a 28-query, 366000-document benchmark suite, used in earlier topic distillation research, reveal two benefits from the new algorithm: distillation quality improves and a by-product of distillation is the ability to extract relevant snippets from hubs which are only partially relevant to the query

Utilizing graph-based representation of text in a hybrid approach to multiple documents summarization

The aim of automatic text summarization is to process text with the purpose of identifying and presenting the most important information appearing in the text. In this research, we aim to investigate automatic multiple document summarization using a hybrid approach of extractive and “shallow abstractive methods. We aim to utilize the graph-based representation approach proposed in [1] and [2] as part of our method to multiple document summarization aiming to provide concise, informative and coherent summaries. We start by scoring sentences based on significance to extract top scoring ones from each document of the set of documents being summarized. In this step, we look into different criteria of scoring sentences, which include: the presence of highly frequent words of the document, the presence of highly frequent words of the set of documents and the presence of words found in the first and last sentence of the document and the different combination of such features. Upon running our experiments we found that the best combination of features to use is utilizing the presence of highly frequent words of the document and presence of words found in the first and last sentences of the document. The average f-score of those features had an average of 7.9% increase to other features\u27 f-scores. Secondly, we address the issue of redundancy of information through clustering sentences of same or similar information into one cluster that will be compressed into one sentence, thus avoiding redundancy of information as much as possible. We investigated clustering the extracted sentences based on two criteria for similarity, the first of which uses word frequency vector for similarity measure and the second of which uses word semantic similarity. Through our experiment, we found that the use of the word vector features yields much better clusters in terms of sentence similarity. The word feature vector had a 20% more number of clusters labeled to contain similar sentences as opposed to those of the word semantic feature. We then adopted a graph-based representation of text proposed in [1] and [2] to represent each sentence in a cluster, and using the k-shortest paths we found the shortest path to represent the final compressed sentence and use it as a final sentence in the summary. Human evaluator scored sentences based on grammatical correctness and almost 74% of 51 sentences evaluated got a perfect score of 2 which is a perfect or near perfect sentence. We finally propose a method for scoring the compressed sentences according to the order in which they should appear in the final summary. We used the Document Understanding Conference dataset for year 2014 as the evaluating dataset for our final system. We used the ROUGE system for evaluation which stands for Recall-Oriented Understudy for Gisting Evaluation. This system compare the automatic summaries to “ideal human references. We also compared our summaries ROUGE scores to those of summaries generated using the MEAD summarization tool. Our system provided better precision and f-score as well as comparable recall scores. On average our system has a percentage increase of 2% for precision and 1.6% increase in f-score than those of MEAD while MEAD has an increase of 0.8% in recall. In addition, our system provided more compressed version of the summary as opposed to that generated by MEAD. We finally ran an experiment to evaluate the order of sentences in the final summary and its comprehensibility where we show that our ordering method produced a comprehensible summary. On average, summaries that scored a perfect score in term of comprehensibility constitute 72% of the evaluated summaries. Evaluators were also asked to count the number of ungrammatical and incomprehensible sentences in the evaluated summaries and on average they were only 10.9% of the summaries sentences. We believe our system provide a \u27shallow abstractive summary to multiple documents that does not require intensive Natural Language Processing.

A framework for the Comparative analysis of text summarization techniques

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceWe see that with the boom of information technology and IOT (Internet of things), the size of information which is basically data is increasing at an alarming rate. This information can always be harnessed and if channeled into the right direction, we can always find meaningful information. But the problem is this data is not always numerical and there would be problems where the data would be completely textual, and some meaning has to be derived from it. If one would have to go through these texts manually, it would take hours or even days to get a concise and meaningful information out of the text. This is where a need for an automatic summarizer arises easing manual intervention, reducing time and cost but at the same time retaining the key information held by these texts. In the recent years, new methods and approaches have been developed which would help us to do so. These approaches are implemented in lot of domains, for example, Search engines provide snippets as document previews, while news websites produce shortened descriptions of news subjects, usually as headlines, to make surfing easier. Broadly speaking, there are mainly two ways of text summarization – extractive and abstractive summarization. Extractive summarization is the approach in which important sections of the whole text are filtered out to form the condensed form of the text. While the abstractive summarization is the approach in which the text as a whole is interpreted and examined and after discerning the meaning of the text, sentences are generated by the model itself describing the important points in a concise way