Create Text Summary Using Python Without NLP Libraries

Create Text Summary Using Python Without NLP Libraries

The simplest way to summarize your text using Python.There are several NLP libraries to work with, for example, Natural Language Toolkit (NLTK), TextBlob, CoreNLP, Gensim, and spaCy. You can use these incredible libraries for processing your text. There are a lot of methods for summarizing texts. In this article, I’m going to show you the easiest way to summarize your texts into three sentences without using any of the NLP libraries.

There are several NLP libraries to work with, for example, Natural Language Toolkit (NLTK), TextBlobCoreNLPGensim, and spaCy. You can use these incredible libraries for processing your text.

There are a lot of methods for summarizing texts. In this article, I’m going to show you the easiest way to summarize your texts into three sentences without using any of the NLP libraries.

However, we will need some libraries for pre-processing and sorting the data.


Libraries Required

import re
import heapq

Suppose we are going to summarize the following text block, which has 600 words:

“ Up to the 1980s, most natural language processing systems were based on complex sets of hand-written rules. Starting in the late 1980s, however, there was a revolution in natural language processing with the introduction of machine learning algorithms for language processing. This was due to both the steady increase in computational power (see Moore’s law) and the gradual lessening of the dominance of Chomskyan theories of linguistics (e.g. transformational grammar), whose theoretical underpinnings discouraged the sort of corpus linguistics that underlies the machine-learning approach to language processing.[3] Some of the earliest-used machine learning algorithms, such as decision trees, produced systems of hard if-then rules similar to existing hand-written rules. However, part-of-speech tagging introduced the use of hidden Markov models to natural language processing, and increasingly, research has focused on statistical models, which make soft, probabilistic decisions based on attaching real-valued weights to the features making up the input data. The cache language models upon which many speech recognition systems now rely are examples of such statistical models. Such models are generally more robust when given unfamiliar input, especially input that contains errors (as is very common for real-world data), and produce more reliable results when integrated into a larger system comprising multiple subtasks.Many of the notable early successes occurred in the field of machine translation, due especially to work at IBM Research, where successively more complicated statistical models were developed. These systems were able to take advantage of existing multilingual textual corpora that had been produced by the Parliament of Canada and the European Union as a result of laws calling for the translation of all governmental proceedings into all official languages of the corresponding systems of government.

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