Text Summarization | NLTK Corpus StopWords |

music by: NCS
#naturallanguageprocessing #artificialintelligence #machinelearning #textprocessing
This code will first tokenize the text into words. Then, it will remove the stop words from the text. Stop words are common words that do not add much meaning to the text, such as "the", "and", and "of". After removing the stop words, the code will calculate the score of each sentence. The score of a sentence is the number of words in the sentence that are not stop words. The sentence with the highest score is the most important sentence.
The code will then return the most important sentence as the summary of the text.
The code will then return the most important sentence as the summary of the text. The code will also plot the score of each sentence.
This code will first summarize the text using the text_summarization() function. Then, it will create a list of all the sentences in the text. Finally, it will plot the scores of the sentences using the plt.bar() function.
In natural language processing, stop words are common words that do not add much meaning to the text. These words are often used to connect other words or to fill in space. Some examples of stop words include "the", "and", "of", "to", and "is".
nltk.corpus is a module in the Natural Language Toolkit (NLTK) that provides access to a variety of corpora, or collections of text. These corpora can be used for a variety of tasks, such as text analysis, machine learning, and natural language generation.
The nltk.corpus module provides a way to access these corpora and to use them for different tasks. For example, you could use the nltk.corpus module to find all of the occurrences of the word "the" in a corpus of news articles.
The following is a long text that can be used for summary:
The history of artificial intelligence (AI) can be traced back to the early days of computing. In 1950, Alan Turing published a paper titled "Computing Machinery and Intelligence" in which he proposed a test to determine whether a machine could be considered intelligent. The Turing test is still used today as a benchmark for AI research.
In the early days of AI, research was focused on developing symbolic AI systems. Symbolic AI systems were based on the idea that intelligence could be represented by symbols and rules. These systems were able to solve some complex problems, but they were not able to scale to real-world applications.
In the 1980s, there was a shift in AI research towards developing connectionist AI systems. Connectionist AI systems were based on the idea that intelligence could be modeled by the connections between neurons in the brain. These systems were able to learn from data and were more robust than symbolic AI systems.
In recent years, there has been a renewed interest in symbolic AI. This is due in part to the development of new techniques for representing knowledge and reasoning. Symbolic AI systems are now being combined with connectionist AI systems to create hybrid systems that are more powerful than either approach on its own.
AI is now being used in a wide variety of applications, including healthcare, finance, transportation, and manufacturing. As AI technology continues to develop, it is likely that we will see even more applications for AI in the future.
This text is long enough to be considered a complex text. It covers a broad range of topics, including the history of AI, the different types of AI systems, and the applications of AI. This text would be a good candidate for summarization because it could be condensed into a shorter version that still conveys the main points of the text.
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