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Built From Scratch Pdf

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built from scratch pdf

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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.

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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.

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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.

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Scratch Built RC Airplane

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