What is AI Text Summarization

An AI Text Summarization Task refers to using artificial intelligence to automatically create a shorter version of a given text while preserving its key information, main ideas, and context. The goal is to produce a concise summary that conveys the essence of the original content without losing its meaning. There are two main types of summarization in AI:

AI Text Summarization
Figure 1 - AI Text Summarization

Where can you find AI summarization models

This is the link to use to filter Hunggingface models for text summarization:

https://huggingface.co/models?pipeline_tag=summarization&sort=trending

Our favourite Model Authors:

The most interesting AI text summarization project

One of the most interesting AI text classification projects is called PEGASUS for Financial Summarization.

This model was fine-tuned on a novel financial news dataset, which consists of 2K articles from Bloomberg, on topics such as stock, markets, currencies, rate and cryptocurrencies.

It is based on the PEGASUS model and in particular PEGASUS fine-tuned on the Extreme Summarization (XSum) dataset: google/pegasus-xsum model. PEGASUS was originally proposed by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu in PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization.

Note: This model serves as a base version. For an even more advanced model with significantly enhanced performance, please check out our advanced version on Rapid API. The advanced model offers more than a 16% increase in ROUGE scores (similarity to a human-generated summary) compared to our base model. Moreover, our advanced model also offers several convenient plans tailored to different use cases and workloads, ensuring a seamless experience for both personal and enterprise access.

Model link:

https://huggingface.co/human-centered-summarization/financial-summarization-pegasus

1. Extractive Summarization

In extractive summarization, the AI selects important sentences, phrases, or words directly from the original text and combines them to form a summary. It doesn’t generate new sentences; it simply picks and highlights the most relevant parts of the input text.

  • How it works: The AI uses algorithms to analyze the input text and score different parts based on factors like sentence importance, frequency of words, and the structure of the document. It then extracts the most important sentences without changing their original wording.
  • Example: If the input is a long article, the AI might select the first and last sentences of each paragraph to form the summary.

Example of Extractive Summarization:

Original text:
"Artificial intelligence (AI) refers to the simulation of human intelligence in machines. These machines are programmed to think like humans and mimic their actions. AI has various applications such as robotics, machine learning, and natural language processing."
Extracted summary:
"AI refers to the simulation of human intelligence in machines. AI has applications such as robotics, machine learning, and natural language processing."

2. Abstractive Summarization

In abstractive summarization, the AI generates a new summary by understanding the main ideas and rephrasing the content using its own words. This is more advanced and closer to how humans summarize content. The model doesn't just select important sentences but interprets the text and produces a summary that may not directly use the exact words or sentences from the original text.

  • How it works: Using deep learning models like transformers, the AI is trained to understand the context and meaning of the text. The model generates new sentences that convey the same message but in a more concise form.
  • Example: If the input is an article, the AI might reword the text and provide a shorter version without copying whole sentences from the original.

Example of Abstractive Summarization:

Original text:
"Artificial intelligence (AI) refers to the simulation of human intelligence in machines. These machines are programmed to think like humans and mimic their actions. AI has various applications such as robotics, machine learning, and natural language processing."
Generated summary:
"AI simulates human intelligence and is used in fields like robotics and machine learning."

AI Summarization Task Pipeline

To perform an AI summarization task, the following steps are typically involved:

  1. Preprocessing: The text needs to be cleaned and processed. This includes:
    • Tokenizing the text (splitting it into words or sentences).
    • Removing unnecessary information like stopwords, punctuation, or special characters.
    • Handling complex structures such as abbreviations or symbols.
  2. Model Selection:
    • Extractive models use statistical or graph-based approaches (e.g., TextRank) to rank sentences and select the most important ones.
    • Abstractive models are based on neural networks (e.g., transformers like BART or T5) that generate new summaries.
  3. Model Training (if necessary): For abstractive summarization, models can be pre-trained on large datasets and fine-tuned on domain-specific data to improve performance. Pre-trained models, like BART or T5, are often fine-tuned on summarization datasets such as CNN/DailyMail or XSum.
  4. Summarization: The model processes the input text and produces a summary based on the chosen approach (extractive or abstractive).
  5. Post-processing: The generated summary might undergo some additional cleaning or formatting to ensure it’s readable and coherent.

Use Cases of AI Summarization

  • News Summarization: Summarizing long news articles into short, readable briefs.
  • Legal and Medical Summarization: Summarizing lengthy legal or medical documents to extract key information.
  • Research Papers: Condensing research papers into abstracts or summaries to help researchers quickly grasp the content.
  • Customer Reviews: Summarizing large sets of customer reviews to highlight key opinions or sentiments.

Challenges in AI Summarization

  • Coherence: For abstractive summarization, the generated summary needs to maintain logical flow and coherence.
  • Preserving Meaning: The AI needs to ensure that the summarized text doesn’t lose the core meaning or introduce inaccuracies.
  • Context Awareness: The model must be capable of understanding context, especially in domain-specific applications (e.g., legal or medical).

Popular Models for AI Summarization

  • BART: A transformer-based model that excels at abstractive summarization.
  • T5: Another transformer that frames all NLP tasks (including summarization) as text-to-text problems.
  • Pegasus: A model designed specifically for long-document summarization.
  • BERTSUM: An extractive summarization model based on BERT.

Evaluation Metrics

  • ROUGE (Recall-Oriented Understudy for Gisting Evaluation): Measures overlap between the generated summary and the reference summary in terms of n-grams (e.g., words or sequences of words).
  • BLEU (Bilingual Evaluation Understudy): Mostly used for translation but can be applied to summarization to check for word overlaps.

Example Workflow (Using Hugging Face)


from transformers import pipeline

# Load a pre-trained summarization model
summarizer = pipeline("summarization")

# Input text to summarize
text = """
Artificial intelligence (AI) refers to the simulation of human intelligence in machines.
These machines are programmed to think like humans and mimic their actions. AI has various applications such as robotics, machine learning, and natural language processing.
"""

# Generate a summary
summary = summarizer(text, max_length=50, min_length=25, do_sample=False)
print(summary[0]['summary_text'])

Conclusion

AI summarization automates the process of summarizing content, making it faster and more scalable. Extractive models are simpler and reliable for picking out key sentences, while abstractive models provide more flexibility by generating human-like summaries. The choice of model depends on the complexity and requirements of the task at hand.

How to setup a text summarization system

If you are ready to setup your first text summarization system follow the instructions in our next page:

How to setup an AI text summarization system

Image sources

Figure 1: https://cdn.prod.website-files.com/5fdc17d51dc102ed1cf87c05/647d0ce837b1920dbe2dcd92_46b79fdd.png

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