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TLBO (Teaching Learning Based Optimization Algorithm) Search Algorithm

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TLBO (Teaching Learning Based Optimization Algorithm) Search Algorithm: TLBO search is an optimization algorithm used in Natural Language Processing. Teaching learning-based optimization (TLBO) is a population-based meta-heuristic optimization technique that simulates the environment of a classroom to optimize a given objective function and it was proposed by R.V. Rao et al. in 2011. Like other nature-inspired algorithms, TLBO is also a population-based method and uses a population of solutions to proceed to the global solution. The population is considered as a group of learners or a class of learners.  The process of TLBO is divided into two parts: the first part consists of the ‘Teacher Phase’ and the second part consists of the ‘Learner Phase’. ‘Teacher Phase’ means learning from the teacher and ‘Learner Phase’ means learning by the interaction between learners. In a classroom, the teacher puts his hard work and makes all the learners of a class educated. Then the learners inte...
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Cuckoo Search Algorithm

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What is Cuckoo Search Algorithm? Cuckoo search is an optimization algorithm used in Natural Language Processing. It was inspired by the obligate brood parasitism of some cuckoo species by laying their eggs in the nests of host birds of other species. Some host birds can engage direct conflict with the intruding cuckoos.  For example, if a host bird discovers the eggs are not their own, it will either throw these alien eggs away or simply abandon its nest and build a new nest elsewhere. Cuckoo search idealized such breeding behavior, and thus can be applied for various optimization problems. It has been shown that cuckoo search is a special case of the well-known (μ + λ)-evolution strategy. Cuckoo search (CS) uses the following representations: Each egg in a nest represents a solution, and a cuckoo egg represents a new solution. The aim is to use the new and potentially better solutions (cuckoos) to replace a not-so-good solution in the nests. In the simplest form, each nest has one...
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Types of Scoring Techniques for Extractive Text Summarization

  Scoring Techniques: Proper noun Sentence length character Sentence length words Sentence position Word frequency Numerical value Named entity Iterative query score Cue words  Word frequency: As the name suggests the more the frequency of the word in the sentence the higher will be it's score. In other words the sentence containing the most frequent words of the document has high chances of getting selected for the Final summary. This is based on the assumption that the higher the frequency of the word in the text , it is more likely to be related to the subject of the document. S(L) = N(w) / N(d) Where, N(w) =  Sum of the frequency of the words of the sentence N(d) = Sum of the frequency of the words of the document. Proper noun: Proper noun refers to an individual,place or organisation. It is considered to be carrying greater information from the rest of the words. The Sentence containing a higher number of proper nouns is more  likely to be selected for the final...
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Why Text Summarization is needed in today's time

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  Why it is needed? With the present explosion of data circulating the digital space, which is mostly non-structured textual data, there is a need to develop automatic text summarization tools that allow people to get insights from them easily.  Currently, we enjoy quick access to enormous amounts of information. However, most of this information is redundant, insignificant, and may not convey the intended meaning. Therefore, using automatic text summarizers capable of extracting useful information that leaves out inessential and insignificant data is becoming vital. Implementing summarization can enhance the readability of documents, reduce the time spent in researching for information, and allow for more information to be fitted in a particular area. The amount of information available has significantly increased because of the Internet's explosive expansion, particularly in the area of text materials. Text summarizing is a technique for creating summaries of text documents ...
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Different Types of Text Summarization

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  Broad Classification: •            Single Document Summarization Ø    Given a single document to generate a summary   •            Multiple Document Summarization Ø    Given a group of documents to generate a summary out of it   •            Generic Summarization Ø    Summarize the content of a document   •            Domain Specific Ø    Text summarization aims at extracting the essential information from a text to produce a shorter version,  such as generating headlines for news and subject lines for emails.   •            Query-focused Summarization Ø    Summarize a document with respect to an information need expressed in a user qu...
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Introduction to Text Summerization

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What is Text Summerization ? Text Summerization is the process of summarizing long text into short piece of text. It's main focus is to only add necessary and important sentences and text into the final summary. The goal of Text Summerization is to provide a shorter version of the source text containing only important aspects of the source text. It can be used in if we want to get only important information from a very long source text.  Why Text Summerization is used ? Text Summerization provides us a shorter version of long text containing only the important and necessary points and aspects of the source file. It helps in ignoring irrelevant information from the source text. It saves the reader's time by reducing the text size. It is mainly used to make precise documents of  long publications. How Text Summerization summerize the text ? Firstly it separates the document sentence wise. Then it uses various word scoring and sentence scoring techniques to decide score's of i...
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