Ships from and sold by bapoperetu.gq When a friend told Bernie Marcus and Arthur Blank that "you've just been hit in the ass by a golden horseshoe," they thought he was crazy. Built from Scratch is the story of how two incredibly determined and creative people--and their associates. Built from Scratch: How a Couple ofRegular Guys Grew the Home Depot from Nothing to$30 Billion. Bemie Malt:us andArthur Blank, with Bob Andelman. From Nothing To 30 Billion free pdf, Download Built From Scratch How A Regular Guys Grew The Home Depot From Nothing To 30 Billion Pdf, Read Online.
|Language:||English, Spanish, Indonesian|
|Genre:||Politics & Laws|
|Distribution:||Free* [*Sign up for free]|
built from scratch pdf. Quote. Postby Just» Tue Aug 28, am. Looking for built from scratch pdf. Will be grateful for any help! Top. from Scratch by. Nick Blundell. School of . 5 Writing, Building, and Loading Your Kernel. . his own operating system, from scratch. So it seems that. Depot Download Pdf, Free Pdf Built From Scratch The Home Depot built from scratch by chelsea knorr, associate editor, dentaltown.
If you have previously obtained access with your personal account, Please log in. If you previously downloadd this article, Log in to Readcube.
Log out of Readcube. Click on an option below to access. Log out of ReadCube. Volume 18 , Issue 3. Please check your email for instructions on resetting your password. If you do not receive an email within 10 minutes, your email address may not be registered, and you may need to create a new Wiley Online Library account. If the address matches an existing account you will receive an email with instructions to retrieve your username.
Search for more papers by this author. First published: Tools Request permission Export citation Add to favorites Track citation. Share Give access Share full text access.
Each topic is scored by the subtraction of its weight in old and new texts.
Thus, a topic gets a high score if it is more relevant in new texts than others. Iteratively, the best weighted sentence from the topic with the highest score is selected to the summary, and the weights are recalculated. Huang and He, and Li et al. As an example, [ 34 ] defines the following topics: emergent topics present only in new texts ; active topics present on both collections, but more relevant in new texts ; not active topics more relevant in old texts ; and extinct topics present only in old texts.
These methods use different features in order to select the sentences for the summary. Huang and He [ 34 ] use word frequencies and [ 35 ] apply the maximal marginal relevance MMR [ 36 ] approach, which assumes that a good sentence must be similar to a target and dissimilar to another one, as the new and old texts, respectively. Both first select the sentences related to the topics with higher weights in the new texts.
Delort and Alfonseca [ 37 ] show a method based on probabilistic topic models, called DualSum. Each text in this approach is represented by a bag of words, and each word is associated with a latent topic similar to the LDA model. DualSum, which has a procedure similar to the TopicSum system [ 38 ], learns a distribution of topics that are organized into the following categories: general topic, which works as a language model in order to identify irrelevant information; topics for collections A and B, in which they represent the subjects that are more present in the old and new texts, respectively; and document specific topics.
After this learning step, DualSum finds an output update summary with topics closest to a target distribution, which is based on the intuition that a good summary may be more similar to its respective texts in the collection B. At this point, it is also important to comment on how DualSum and also other summarization methods compare distributions. One of the most used metrics for comparing distributions is the Kullback-Leibler KL divergence see.
We introduce in more details such strategy in the next section, as we have extended it for our tests on update summarization. Methods based on graph models have been widely investigated in automatic summarization see, e. To the best of our knowledge, in the context of US, the most expressive results were reached by the positive and negative reinforcement PNR2 system [ 47 ]. PNR2 uses a graph for text modeling, in which each node indicates a sentence and each edge between two sentences is weighted by their Cosine similarity [ 48 ].
In PNR2, given a graph that represents a text collection, its procedure runs an optimization algorithm in which the sentences share scores among themselves based on their similarities with positive and negative reinforcements. This way, a sentence receives a more positive score if it is more similar to sentences from new texts.
In the experiments reported in [ 47 ], PNR2 outperforms the PageRank [ 49 ] algorithm, which the authors have also experimented for the US task. Some other recent initiatives tried to use integer linear programming to combine relevant summarization features and to properly deal with redundancy treatment in the summarization process see, e.
There are also some attempts to use US for specific situations, as to follow the news about human tragedies and disasters [ 52 ].
This kind of application seems a natural way to follow in the area. All the previous efforts focused on the English language. To the best of our knowledge, the only previous work for Portuguese is our preliminary effort reported on [ 15 ], where we have tested some US methods. This paper builds upon this previous initiative by reporting new summarization strategies and their cross-lingual evaluation, which we start detailing in the next section.
Besides the methods that we briefly described in the previous section, we have also tested two more methods, which we introduce in what follows. An enriched version of KLSum: introducing subtopics Hearst and Koch [ 53 , 54 ] define a textual topic as the main subject or theme in a text, and this topic may be divided into minor portions, its subtopics, which contribute to the main topic.
Therefore, the subtopics in a text are the components of its main subject 4. A subtopic may be expressed by a coherent textual portion with one or more sentences in a row in a text.
Thus, we may handle the identification of subtopics as a text segmentation task, in which each identified segment is a subtopic. In this example, we may see a text about an airplane crash segmented into three subtopics: sentences from 1 to 5; sentence 6; and sentence 7. The first subtopic is about the accident itself, while the others present more details about the airplane and its crew, respectively. Table 1 Example of text segmented into subtopics [S1 ] A plane crash in Bukavu, in the Eastern Democratic Republic of Congo, killed 17 people on Thursday afternoon, said the spokesman of the United Nations.
To automatically segment texts into their subtopics, several approaches were proposed in the literature see, e. Of special interest to us is the TextTiling algorithm [ 53 ]. Basically, TextTiling analyzes each sentence pair following the reading flow in order to identify significant vocabulary changes that may indicate subtopic boundaries. It has a good performance and is among the most used ones in the area. Such strategy was recently adapted for the Portuguese language, as reported by [ 57 , 58 ], also performing well.
Some other strategies for this language do exist, as the one that correlates discourse structure following the RST model with subtopic changes in a text [ 59 ], but they are more expensive and of less general application than the previous one.
Since subtopics have recently shown to be very useful in summarization see, e. We have included subtopics into the KLSum strategy, which is used by several summarization systems, as already commented in the previous section.
Website builders are very flexible these days. They understand their purpose is to make web design easy. You can almost always play around with templates as part of the free trials, so you can get a sense of how easy customization is without spending a cent.
Slow down there. Knowing how to make a website is one thing. Publishing your website blindly is another. Always preview changes to your website before publishing them. You need to be sure things are working the way you want them to. Some of the key questions to ask are: Is all the spelling and grammar correct?
Are all the buttons on the menu working? Does your site fulfill a purpose?
Is your formatting consistent? Does it function on desktop and mobile phone screens? Does the site load quickly?