NLP for Text Summarization: Creating a Summarization Model

Are you tired of reading through long and tedious articles? Do you wish you could quickly extract the main points of a text and move on with your day? If so, then text summarization is the answer! Natural Language Processing (NLP) has made great strides in recent years, and one of its most exciting applications is text summarization.

In this article, we will guide you through the process of creating an NLP-based summarization model. We will cover the basics of NLP, the various types of summarization techniques, and the steps involved in building a summarization model. By the end of this article, you will have a good understanding of how to quickly summarize any text with your own NLP model.

Understanding NLP

NLP is a branch of Artificial Intelligence that deals with understanding and processing human language. It enables machines to analyze and interpret natural language data, including texts, spoken words, and even handwriting. NLP can be used to perform various tasks like sentiment analysis, language translation, speech recognition, and text summarization.

To create an NLP-based summarization model, we need to understand the different NLP techniques used for summarization.

Types of Summarization Techniques

There are two main types of summarization techniques: extractive and abstractive.

Extractive summarization is the process of selecting the most important sentences from a text and presenting them as a summary. Extractive summarization is the simplest form of summarization and is based solely on statistical and linguistic patterns found in the original text.

Abstractive summarization, on the other hand, aims to rewrite the original text in a shorter form, while still retaining its original meaning. Abstractive summarization is a more challenging task than extractive summarization, as it requires the model to have a deep understanding of the text and generate new text based on that understanding.

In our article, we will focus on extractive summarization, as it is easier to understand and implement.

Steps for Creating a Summarization Model

To create a summarization model, we need to follow these steps:

  1. Gather a large dataset of texts to train our model.

  2. Preprocess the dataset to remove noise and unwanted information.

  3. Use NLP techniques to extract important features from the texts.

  4. Train a machine learning model on the extracted features.

  5. Evaluate the model’s performance and fine-tune it.

  6. Deploy the model and use it to summarize new texts.

Dataset Collection

The first step in creating a summarization model is to gather a large dataset of texts. The dataset should cover a wide range of topics and be of varying lengths. This ensures that the summarization model can work with any type of text and produce relevant summaries.

There are various sources from which we can collect datasets. Some of the popular sources include news articles, research papers, and social media posts. Once we have our dataset, we move to the next step of preprocessing.


Preprocessing involves cleaning the dataset by removing unwanted information and noise. Some of the preprocessing techniques for text summarization include:

  1. Removing Stopwords: Stopwords are words that do not add any value to the text and can be safely removed without affecting its meaning. For example, words like “the”, “is”, and “and” are stopwords.

  2. Stemming and Lemmatization: Stemming and Lemmatization are techniques to reduce words to their root form. For example, words like “playing”, “played”, and “plays” can be reduced to the root word “play”.

  3. Removing Punctuation: Punctuation marks like commas, periods, and exclamation marks can be safely removed from the text as they do not add any valuable information.

Once we have preprocessed our data, we move on to the feature extraction phase.

Feature Extraction

The key to creating a good summarization model is the selection of features used to represent the text. The features should capture the essence of the text and be relevant to the task of summarization.

There are several techniques for feature extraction, some of which are:

  1. Term Frequency-Inverse Document Frequency (TF-IDF): This technique assigns a weight to each word in the text based on how often it appears in the text and how unique it is to the document.

  2. TextRank: This technique is based on Google’s PageRank algorithm and assigns a score to each sentence based on its importance in the text.

  3. Latent Semantic Analysis (LSA): This technique uses Singular Value Decomposition (SVD) to reduce the dimensionality of the text and represent it in a lower-dimensional space.

Once we have selected our feature extraction technique, we move on to the training phase.


To train our summarization model, we need to use a machine learning algorithm. Some of the popular algorithms used for text summarization include:

  1. Support Vector Machines (SVM): SVM is a supervised learning algorithm that can be used for both classification and regression tasks. It is a popular algorithm for text classification and summarization.

  2. Random Forest: Random forest is an ensemble learning algorithm that combines multiple decision trees to improve performance. It is a popular algorithm for natural language processing tasks, including text summarization.

  3. Convolutional Neural Networks (CNNs): CNNs are a type of neural network that are widely used for image analysis. However, they can also be adapted for text summarization by treating the text as a sequence of words.

Once we have trained our model, we move on to the evaluation phase.


Evaluation involves measuring the performance of the summarization model. The two main metrics used for evaluation are ROUGE (Recall-Oriented Understudy for Gisting Evaluation) and BLEU (Bilingual Evaluation Understudy).

ROUGE measures the similarity between the generated summary and the reference summary, while BLEU measures the similarity between the generated summary and the original text.

Once we have evaluated our model, we can fine-tune it and deploy it.


Deployment involves using the model to summarize new texts. We can deploy our summarization model as a web application, an API, or even as a plugin for text editors like Microsoft Word.

With our summarization model deployed, we can now take any text and quickly extract its main points within seconds. This is the power of NLP-based text summarization.


In conclusion, NLP-based text summarization is an exciting application of NLP that can save time and improve productivity. With the steps outlined in this article, anyone can create their own summarization model and start summarizing texts like a pro.

So, what are you waiting for? Get started with NLP-based text summarization today and take your summarization game to the next level.

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