Multi-document summarization is an automatic procedure aimed at extraction of information from multiple texts written about the same topic. The resulting summary report allows individual users, such as professional information consumers, to quickly familiarize themselves with information contained in a large cluster of documents.
In such a way, multi-document summarization systems are complementing the news aggregators performing the next step down the road of coping with information overload. Multi- document summarization creates information reports that are both concise and comprehensive.
While the goal of a brief summary is to simplify information search and cut the time by pointing to the most relevant source documents, comprehensive multi-document summary should itself contain the required information, hence limiting the need for accessing original files to cases when refinement is required. Automatic summaries present information extracted from multiple sources algorithmically, without any editorial touch or subjective human intervention, thus making it completely unbiased.
The multi-document summarization task is more complex than summarizing a single documenteven a long one. The difficulty arises from thematic diversity within a large set of documents.
A good summarization technology aims to combine the main themes with completeness, readability, and concision. The Document Understanding Conferences,  conducted annually by NISThave developed sophisticated evaluation criteria for techniques accepting the multi-document summarization challenge. An ideal multi-document summarization system not only shortens the source texts, but also presents information organized around the key aspects to represent diverse views.
Success produces an overview of a given topic. Such text compilations should also basic requirements for an overview text compiled by a human. The multi-document summary quality criteria are as follows:. The latter point deserves an additional note. Care is taken to ensure that the automatic overview shows:. The multi-document summarization technology is now coming of age - a view supported by a choice of advanced web-based systems that are currently available.
As auto-generated multi-document summaries increasingly resemble the overviews written by a human, their use of extracted text snippets may one day face copyright issues in relation to the fair use copyright concept. From Wikipedia, the free encyclopedia. This article needs additional citations for verification. Please help improve this article by adding citations to reliable sources.
Unsourced material may be challenged and removed.Automatic summarization is the process of shortening a set of data computationally, to create a subset a summary that represents the most important or relevant information within the original content. In addition to text, images and videos can also be summarized.
Text summarization finds the most informative sentences in a document; image summarization finds the most representative images within an image collection [ citation needed ] ; video summarization extracts the most important frames from the video content.
There are two general approaches to automatic summarization: extraction and abstraction. Here, content is extracted from the original data, but the extracted content is not modified in any way. Examples of extracted content include key-phrases that can be used to "tag" or index a text document, or key sentences including headings that collectively comprise an abstract, and representative images or video segments, as stated above.
For text, extraction is analogous to the process of skimming, where the summary if availableheadings and subheadings, figures, the first and last paragraphs of a section, and optionally the first and last sentences in a paragraph are read before one chooses to read the entire document in detail.
This has been applied mainly for text. Abstractive methods build an internal semantic representation of the original content, and then use this representation to create a summary that is closer to what a human might express. Abstraction may transform the extracted content by paraphrasing sections of the source document, to condense a text more strongly than extraction. Such transformation, however, is computationally much more challenging than extraction, involving both natural language processing and often a deep understanding of the domain of the original text in cases where the original document relates to a special field of knowledge.
Approaches aimed at higher summarization quality rely on combined software and human effort.
In Machine Aided Human Summarization, extractive techniques highlight candidate passages for inclusion to which the human adds or removes text. In Human Aided Machine Summarization, a human post-processes software output, in the same way that one edits the output of automatic translation by Google Translate.
There are broadly two types of extractive summarization tasks depending on what the summarization program focuses on. The first is generic summarizationwhich focuses on obtaining a generic summary or abstract of the collection whether documents, or sets of images, or videos, news stories etc. The second is query relevant summarizationsometimes called query-based summarizationwhich summarizes objects specific to a query. Summarization systems are able to create both query relevant text summaries and generic machine-generated summaries depending on what the user needs.
An example of a summarization problem is document summarization, which attempts to automatically produce an abstract from a given document. Sometimes one might be interested in generating a summary from a single source document, while others can use multiple source documents for example, a cluster of articles on the same topic. This problem is called multi-document summarization. A related application is summarizing news articles. Imagine a system, which automatically pulls together news articles on a given topic from the weband concisely represents the latest news as a summary.
Image collection summarization is another application example of automatic summarization. It consists in selecting a representative set of images from a larger set of images. Video summarization is a related domain, where the system automatically creates a trailer of a long video.
This also has applications in consumer or personal videos, where one might want to skip the boring or repetitive actions. Similarly, in surveillance videos, one would want to extract important and suspicious activity, while ignoring all the boring and redundant frames captured. At a very high level, summarization algorithms try to find subsets of objects like set of sentences, or a set of imageswhich cover information of the entire set. This is also called the core-set. These algorithms model notions like diversity, coverage, information and representativeness of the summary.
Query based summarization techniques, additionally model for relevance of the summary with the query. Some techniques and algorithms which naturally model summarization problems are TextRank and PageRank, Submodular set functionDeterminantal point processmaximal marginal relevance MMR etc.
The task is the following.
You are given a piece of text, such as a journal article, and you must produce a list of keywords or key[phrase]s that capture the primary topics discussed in the text. For example, news articles rarely have keyphrases attached, but it would be useful to be able to automatically do so for a number of applications discussed below.
Consider the example text from a news article:.From Wikipedia, the free encyclopedia. The present disambiguation page holds the title of a primary topicand an article needs to be written about it. It is believed to qualify as a broad-concept article.
It may be written directly at this page or drafted elsewhere and then moved over here. Related titles should be described in Summarywhile unrelated titles should be moved to Summary disambiguation. For other uses, see Recap disambiguation. Disambiguation page providing links to topics that could be referred to by the same search term. Categories : Disambiguation pages. Hidden categories: Disambiguation pages to be converted to broad concept articles Disambiguation pages with short description All article disambiguation pages All disambiguation pages.
The unrest came as governments and Western institutions in many parts of the Muslim world braced for protests after Friday Prayer — an occasion often associated with demonstrations as worshipers leave mosques. In Tunisia, the authorities invoked emergency powers to outlaw all demonstrations, fearing an outpouring of anti-Western protest inspired both by the American-made film and by cartoons depicting the Prophet Muhammad in a French satirical weekly.
American diplomatic posts in India, Indonesia and elsewhere closed for the day. In Bangladesh, several thousand activists from Islamic organizations took over roads in the center of the capital, Dhaka after prayers. They also burned the American and French flags. The protesters threatened to seize the American Embassy on Saturday, but a police order banned any further demonstrations. Separate protests took place outside of Dhaka as well. European countries took steps to forestall protests among their own Muslim minorities and against their missions abroad.
France had already announced the closure on Friday of embassies and other institutions in 20 countries while, in Paris, some Muslim leaders urged their followers to heed a government ban on weekend demonstrations protesting against denigration of the prophet.
Interior Minister Manuel Valls said officials throughout the country had orders to prevent all protests and crack down if the ban was challenged. Valls said. In Pakistan, the scene of the most turbulent unrest, ARY News said that a driver, Muhammad Amir, was shot three times by the police as he drove through an area where stick-wielding protesters were burning a movie theater owned by a prominent politician.
The station repeatedly broadcast graphic footage of hospital staff giving emergency treatment to Mr. Amir, apparently shortly before he died. Other Pakistani journalists condemned the footage as insensitive and irresponsible. Ashraf called on the United Nations and international community to formulate a law outlawing hate speech across the world. But the scenes of chaos in some parts of the country as the day progressed suggested that the government had failed to control public anger on the issue.
In Peshawar, where the television employee was killed, protesters attacked and burned two movie theaters, breaking through the windows with sticks and setting fire to posters that featured images of female movie stars. Television footage showed the police firing in the air to disperse the crowd, and a hospital official said that at least 15 people, including three police officers, were injured.
In Islamabad, where thousands of protesters flooded toward the heavily guarded diplomatic enclave, Express News reported that the police ran out of rubber bullets because of heavy firing. A television reporter said that when protesters in nearby Rawalpindi ran out of material to burn, they broke into several tire shops along a major road to steal fresh supplies. The government cut off cellphone coverage in major cities, while the authorities in Islamabad sealed all exits to the city after Friday Prayer, state radio reported.
Some Pakistanis were relying on e-mail and social media sites, like Twitter, to communicate. Expressions of weary anger over the violence were common. Paracha, a cultural commentator with Dawn newspaper, on Twitter. Large shipping containers blocked roads through the center of several cities. Western diplomatic missions were closed for the day. Summarize in about sentences.
Free Summarizer Summarize any text online in just a few seconds. Ruppert, Chief Summarizer Officer Stop wasting your time and money. Summarize text Read less, do more. Proofread text Improve your text. Summarize any text Copy and paste your loooong text below.Text example Delete text.
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Knowing how to convert betting odds into implied probabilities is fundamental for betting as it helps you assess the potential value on a particular market. Once converted, if the implied probability is less than your assessment, then it represents betting value.
The most common odds formats are decimal, American and fractional. The formulas below explain how to convert odds to implied probabilities. For the examples below we will use Smarkets odds for the 2016 Australian Open final between Andy Murray and Novak Djokovic:As you can see this is the same probability as with the decimal odds.
Because odds in any format are just a different display of the same chance. There are two instances of American odds (positive and negative) which require separate calculations. What is value betting. How to calculate implied probability in betting How to calculate betting margins Why do betting odds change.
How to calculate expected value in betting How to convert betting odds What are the different betting odds formats. How do bookmakers make money How to calculate implied probability in betting Learning how to calculate implied probability from betting odds is key to assessing the potential value in a betting market. Implied probability is a conversion of betting odds into a percentage. It takes into account the bookmaker margin to express the expected probability of an outcome occurring.
For the examples below we will use Smarkets odds for the 2016 Australian Open final between Andy Murray and Novak Djokovic: Player Decimal odds Fractional odds American odds Implied probability Djokovic 1.
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Furthermore, you need to quickly calculate the potential winnings for different bets, especially if the odds are changing while the event unfolds.
Odds tell you the likelihood that an event will occur (a team wins, a boxer makes it a certain round) and how much will be paid out if you win. There are, however, multiple ways to convey this information. Create an accountCommunity DashboardRandom ArticleAbout UsCategoriesRecent ChangesWrite an ArticleRequest a New ArticleAnswer a RequestMore Ideas.
Odds represent which team, horse, or athlete has the highest probability of winning. While there are different ways to write odds, they all indicate how likely one outcome is in comparison to another. When I flip a coin, it is just as likely that I flip heads as tails. The odds are equal, or one to one. The odds are 80 to 20. Otherwise put, it is four times more likely that it will rain than stay sunny. Because circumstances may change spontaneously, odds may change as well.
They are not an exact science. The most common use of odds is found when placing a bet on a sporting event. Betting agencies use historical data and team statistics to predict who is more likely to win. Whoever has the highest odds is considered the "favorite. Betting on the underdog is riskier than betting on a favorite, but a higher risk means a higher potential reward. The "longer the odds," or the less likely, the more money you could win.
Many racetracks and betting establishments will have a booklet or pamphlet helping you learn terminology, but you should understand the lingo before you read odds. Some of the basic words include: Action: A bet or wager of any kind or amount.
Bookie: Someone who accepts bets and sets odds. Hedging: Placing bets on the team with the high odds, and the low odds, to minimize loss. Line: On any event, the current odds or point spreads on the game. Wager: The money you pay, or risk, on an outcome or event. Odds of 3-5 indicate that your profit will be three-fifths of a dollar.
To determine profit, multiply the amount you bet by the fraction. This makes sense, because you would expect a bet on the underdog to have a higher payout. If you have a hard time with fractions, then see if there is a larger number on top then on bottom. When you bet for the underdog, it is called betting "against the odds. Odds are presented as a positive or negative number next to the team's name.
A negative number means the team is favored to win, while a positive number indicates that they are the underdog. This means the Cowboys are the favorites, but pay out less money if a bet on them wins. Try out an online to check your math when you first get started.How to Generate Your Own Wikipedia Articles (LIVE)
Soon enough it will be second nature, but for now ask a friend or search for a calculator that fits your betting needs. You also get the money you bet back.