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What are the best ways to summarize text in NLP?

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Text summarization is a challenging and useful task in natural language processing (NLP) that aims to produce a concise and coherent summary of a longer text. It can help you save time, extract key information, and generate insights from large amounts of text. But what are the best ways to summarize text in NLP? In this article, you will learn about some of the most common and effective methods, their advantages and disadvantages, and how to apply them to your own projects.

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Somesh Chatterjee

Sr. Staff Data Scientist
Sanjay Kumar MBA,MS,PhD

1 Extraction vs. Abstraction

One way to categorize text summarization methods is based on whether they use extraction or abstraction. Extraction means selecting the most relevant sentences or phrases from the original text and concatenating them into a summary. Abstraction means generating new sentences or words that capture the main idea of the text, using techniques such as paraphrasing, generalization, or synthesis. Extraction is faster and simpler, but it can result in redundant or incoherent summaries. Abstraction is more complex and creative, but it can introduce errors or distortions.

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Sanjay Kumar MBA,MS,PhD
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Text summarization methods can be categorized into two main approaches: extraction and abstraction. Extraction involves selecting the most relevant sentences or phrases directly from the original text and combining them into a summary. This method is relatively faster and simpler to implement but may lead to summaries that are somewhat redundant or incoherent. Abstraction, on the other hand, entails generating entirely new sentences or words to capture the main ideas from the text. This approach involves techniques like paraphrasing, generalization, or synthesis. Abstraction is more complex and creative but can introduce errors or distortions in the summary.

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2 Supervised vs. Unsupervised

Another way to classify text summarization methods is based on whether they use supervised or unsupervised learning. Supervised learning means training a model on a large corpus of text-summary pairs, using metrics such as ROUGE or BLEU to evaluate the quality of the summaries. Unsupervised learning means applying algorithms such as clustering, ranking, or graph-based methods to identify the most salient parts of the text, without relying on any labeled data. Supervised learning can produce more accurate and fluent summaries, but it requires a lot of annotated data and computational resources. Unsupervised learning can work with any text, but it can produce less coherent and informative summaries.

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Somesh Chatterjee

Sr. Staff Data Scientist
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While supervised techniques may seem to be a lot of effort to get it working but in reality it's not entirely true. For most use cases we will be able to leverage pretrained model for our use cases. A lot of model exists which can be reused directly, you can get them from Huggingface or Tensorflow Zoo or any such location. Even if off the shelf model doesn't do great (may be you are in legal or medical domain where text usually have a lot of domain specific jargons not seen in general text), even then you might be able to fine tune the off the shelf model to meet your needs. Very rarely will you be required to actually train this kind of model from scratch.

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3 Neural vs. Non-Neural

A third way to distinguish text summarization methods is based on whether they use neural or non-neural models. Neural models are based on deep learning architectures such as recurrent neural networks (RNNs), convolutional neural networks (CNNs), or transformers, that can learn complex representations and generate summaries from scratch. Non-neural models are based on rule-based, statistical, or heuristic methods that use predefined features or criteria to extract or generate summaries. Neural models can produce more natural and diverse summaries, but they are more prone to hallucination or repetition. Non-neural models can produce more factual and consistent summaries, but they are more limited and rigid.

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Sanjay Kumar MBA,MS,PhD
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Text summarization methods can also be categorized as neural or non-neural models. Neural models utilize deep learning architectures like recurrent neural networks (RNNs), convolutional neural networks (CNNs), or transformers. These models can learn intricate representations and generate summaries from scratch, resulting in more natural and diverse summaries. However, they are susceptible to issues like hallucination or repetition. Non-neural models, on the other hand, rely on rule-based, statistical, or heuristic approaches that use predefined features or criteria to extract or generate summaries. While these models tend to produce more factual and consistent summaries, they are often more limited and rigid in their output.

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4 How to Choose a Method

When it comes to text summarization in NLP, there is no one-size-fits-all solution. You need to consider the type and length of text you want to summarize, the purpose and audience of the summary, the desired length and style of the summary, the availability and quality of data and labels, and any computational or time constraints. Generally speaking, extraction is best for a quick and simple summary that preserves the original text, abstraction for a more creative and concise summary that paraphrases it, supervised learning for a more accurate and fluent summary with labeled data, unsupervised learning for a more general and flexible summary with little or no labeled data, neural models for a more natural and diverse summary that can handle complex texts, and non-neural models for a more factual and consistent summary that follows specific rules or criteria. Experimenting with different methods and fine-tuning them to your specific needs is necessary to get the best results.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are the best ways to summarize text in NLP?

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5

1 Extraction vs. Abstraction

2 Supervised vs. Unsupervised

3 Neural vs. Non-Neural

4 How to Choose a Method

5 Here’s what else to consider

Machine Learning

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What are the best ways to summarize text in NLP?

1

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5

1 Extraction vs. Abstraction

2 Supervised vs. Unsupervised

3 Neural vs. Non-Neural

4 How to Choose a Method

5 Here’s what else to consider

Machine Learning

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