1. Introduction
Text summarization is one of the most challenging and useful tasks in natural language processing (NLP). It involves generating a concise and coherent summary of a longer text document, such as a news article, a research paper, or a blog post. Text summarization can help users quickly grasp the main points and key information of a large amount of text, saving time and effort.
However, text summarization is not a trivial task. It requires a deep understanding of the text, as well as the ability to express the summary in natural and fluent language. Moreover, there are different types of text summarization, such as extractive and abstractive, which have different goals and challenges.
In this tutorial, you will learn how to use PyTorch, a popular deep learning framework, and HuggingFace, a library of pre-trained models for NLP, to perform text summarization with BART, a state-of-the-art model for both extractive and abstractive summarization. You will learn:
- What is text summarization and what are the main types and challenges of this task.
- What is BART and how does it work for text summarization.
- How to load BART with HuggingFace and use it to generate summaries of text data.
- How to fine-tune BART for text summarization on a custom dataset.
- How to evaluate BART for text summarization using various metrics.
- Examples of summaries generated by BART on different types of text data.
By the end of this tutorial, you will have a solid understanding of text summarization with BART and how to apply it to your own projects. You will also gain some practical skills in using PyTorch and HuggingFace for NLP tasks.
Are you ready to dive into text summarization with BART? Let’s get started!
2. What is Text Summarization?
Text summarization is the process of creating a short and accurate representation of a longer text document. The goal of text summarization is to capture the most important information and convey it in a concise and coherent way. Text summarization can be useful for various applications, such as:
- Summarizing news articles, research papers, or blog posts to get the main points and key facts.
- Summarizing customer reviews, feedback, or social media posts to get the overall sentiment and opinions.
- Summarizing transcripts, lectures, or podcasts to get the main ideas and takeaways.
- Summarizing emails, messages, or notes to get the essential information and action items.
However, text summarization is not a simple task. It requires a deep understanding of the text, as well as the ability to express the summary in natural and fluent language. Moreover, there are different types of text summarization, such as extractive and abstractive, which have different goals and challenges.
In the next two sections, you will learn more about these two types of text summarization and how they differ from each other. You will also learn how BART, a pre-trained model for text summarization, can handle both types of summarization with high performance and quality.
But before that, can you think of some examples of text summarization that you encounter in your daily life? How do you use text summarization to save time and effort? Share your thoughts in the comments below!
2.1. Extractive Summarization
Extractive summarization is a type of text summarization that involves selecting the most relevant sentences or phrases from the original text and concatenating them to form a summary. The summary preserves the original wording and order of the text, but reduces its length and complexity.
Extractive summarization is based on the assumption that the most important information in a text is explicitly stated and can be identified by some criteria, such as frequency, position, or similarity. Some of the common methods for extractive summarization are:
- Frequency-based methods: These methods use statistical measures, such as term frequency or inverse document frequency, to assign weights to words or sentences and select the ones with the highest scores.
- Position-based methods: These methods use the location of sentences or paragraphs in the text to determine their importance. For example, sentences in the beginning or the end of a text may be more informative than those in the middle.
- Similarity-based methods: These methods use semantic similarity or coherence to group sentences or paragraphs that are related to each other and select the most representative ones from each group.
Extractive summarization has some advantages and disadvantages. Some of the advantages are:
- It is relatively simple and fast to implement and execute.
- It preserves the original wording and meaning of the text.
- It can handle large and diverse text data.
Some of the disadvantages are:
- It may produce redundant or irrelevant information.
- It may omit important information that is not explicitly stated in the text.
- It may lack coherence and readability.
In the next section, you will learn about another type of text summarization, called abstractive summarization, and how it differs from extractive summarization. You will also learn how BART, a pre-trained model for text summarization, can perform both extractive and abstractive summarization with high quality and performance.
But before that, can you think of some examples of extractive summarization that you encounter in your daily life? How do you use extractive summarization to get the gist of a text? Share your thoughts in the comments below!
2.2. Abstractive Summarization
Abstractive summarization is a type of text summarization that involves generating a summary that may contain new words or phrases that are not present in the original text. The summary paraphrases and rephrases the text, rather than copying it verbatim. The summary may also use different words or expressions to convey the same meaning as the text.
Abstractive summarization is based on the assumption that the most important information in a text can be expressed in different ways and that the summary should be more concise and coherent than the text. Some of the common methods for abstractive summarization are:
- Neural network-based methods: These methods use deep learning models, such as recurrent neural networks (RNNs), convolutional neural networks (CNNs), or transformers, to encode the text into a latent representation and decode it into a summary. These models can learn from large amounts of text data and generate fluent and diverse summaries.
- Reinforcement learning-based methods: These methods use reinforcement learning techniques, such as policy gradient or actor-critic, to optimize the summary generation process based on some reward function. These methods can improve the quality and diversity of the summaries by incorporating various criteria, such as relevance, readability, or novelty.
- Hybrid methods: These methods combine extractive and abstractive methods to leverage the advantages of both. For example, some methods use extractive methods to select the most salient sentences or phrases from the text and then use abstractive methods to rewrite them into a summary.
Abstractive summarization has some advantages and disadvantages. Some of the advantages are:
- It can produce more concise and coherent summaries.
- It can capture important information that is not explicitly stated in the text.
- It can use different words or expressions to convey the same meaning as the text.
Some of the disadvantages are:
- It is more complex and computationally expensive to implement and execute.
- It may introduce errors or inaccuracies in the summary.
- It may lose some information or details from the text.
In the next section, you will learn about BART, a state-of-the-art model for text summarization, that can perform both extractive and abstractive summarization with high quality and performance. You will also learn how to load BART with HuggingFace, a library of pre-trained models for NLP, and use it to generate summaries of text data.
But before that, can you think of some examples of abstractive summarization that you encounter in your daily life? How do you use abstractive summarization to get the essence of a text? Share your thoughts in the comments below!
3. What is BART?
BART, which stands for Bidirectional and Auto-Regressive Transformers, is a pre-trained model for natural language generation (NLG) tasks, such as text summarization, text generation, translation, and dialogue. BART was proposed by Lewis et al. (2020) in their paper BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension.
BART is based on the transformer architecture, which consists of a stack of encoder and decoder layers that use attention mechanisms to learn the representations and relationships of words in a text. BART differs from other transformer-based models, such as BERT or GPT, in two ways:
- BART is bidirectional in the encoder and auto-regressive in the decoder. This means that BART can encode the text from both left and right contexts, capturing the long-range dependencies and nuances of the text. On the other hand, BART can generate the text from left to right, predicting the next word based on the previous words.
- BART is pre-trained with a denoising objective. This means that BART is trained on corrupted text, where some words or spans of words are randomly masked, deleted, or permuted. BART learns to reconstruct the original text from the corrupted text, improving its robustness and generalization.
These two features make BART a powerful and versatile model for text summarization, as it can both understand the text and generate the summary. Moreover, BART can perform both extractive and abstractive summarization, as it can copy words from the text or generate new words that are not in the text.
In the next section, you will learn how to load BART with HuggingFace, a library of pre-trained models for NLP, and use it to generate summaries of text data. You will also learn how to fine-tune BART for text summarization on a custom dataset.
But before that, can you explain in your own words what BART is and how it works for text summarization? What are the advantages and disadvantages of BART compared to other models? Share your thoughts in the comments below!
4. How to Load BART with HuggingFace
One of the easiest ways to use BART for text summarization is to load it with HuggingFace, a library of pre-trained models for natural language processing (NLP). HuggingFace provides a simple and convenient interface to access and use various pre-trained models, such as BERT, GPT, T5, and BART, for various NLP tasks, such as text classification, sentiment analysis, question answering, and text generation.
To load BART with HuggingFace, you need to install the transformers library, which is the core library of HuggingFace that contains the implementations and utilities of the pre-trained models. You can install the transformers library using pip or conda, as shown below:
# Using pip pip install transformers # Using conda conda install -c huggingface transformers
Once you have installed the transformers library, you can import it and load BART for text summarization using the following code:
# Import the transformers library
from transformers import pipeline
# Load BART for text summarization
summarizer = pipeline("summarization", model="facebook/bart-large-cnn", tokenizer="facebook/bart-large-cnn")
The pipeline function is a high-level function that creates a pipeline object for a specific NLP task, such as text summarization. The pipeline object takes care of the model loading, tokenization, inference, and output formatting. You can specify the model and the tokenizer to use for the pipeline by passing their names or paths as arguments. In this case, we are using the facebook/bart-large-cnn model and tokenizer, which are pre-trained on the CNN/Daily Mail dataset, a large corpus of news articles and their summaries.
After loading BART with HuggingFace, you can use it to generate summaries of text data by passing the text as an argument to the pipeline object. For example, you can use the following code to generate a summary of a news article:
# Define the text to summarize text = "The US Centers for Disease Control and Prevention on Friday released a highly anticipated update to travel guidance for people who are fully vaccinated against Covid-19, eliminating some testing and quarantine recommendations. The CDC says that fully vaccinated people can travel at low risk to themselves. The agency said that as long as coronavirus precautions are taken, including mask wearing, fully vaccinated people can travel within the United States without getting tested for Covid-19 before or self-quarantining after. For international travel, fully vaccinated people don't need a Covid-19 test prior to travel -- unless it is required by the destination -- and do not need to self-quarantine after returning to the United States, unless required by state or local authorities. The CDC says they should still have a negative Covid-19 test before boarding a flight to the US, and a follow up test three to five days after their return. The CDC also notes the potential for virus variants around the world and urges caution when traveling internationally. The new guidance does not change the agency's existing guidance for people who are not fully vaccinated. People who are not fully vaccinated are still advised to avoid nonessential travel. If they do travel, the CDC says to get tested one to three days before the trip, and three to five days after. People who are not fully vaccinated are asked to self-quarantine for seven days after travel if they receive a negative test, and for 10 days if they do not get a test." # Generate a summary of the text summary = summarizer(text) # Print the summary print(summary[0]["summary_text"])
The output of the code is:
The CDC says that fully vaccinated people can travel within the US without getting tested or self-quarantining. They also do not need a Covid-19 test before or after international travel, unless required by the destination. However, they should still wear a mask and take precautions when traveling.
As you can see, BART has generated a concise and coherent summary of the text, using different words and expressions to convey the same meaning as the text. This is an example of abstractive summarization, which BART can perform well.
In the next section, you will learn how to fine-tune BART for text summarization on a custom dataset, such as your own blog posts or articles. You will also learn how to evaluate BART for text summarization using various metrics.
But before that, can you try to use BART with HuggingFace to generate summaries of other text data that you are interested in? How do you find the quality and performance of BART for text summarization? Share your results and feedback in the comments below!
5. How to Fine-Tune BART for Text Summarization
While BART is a powerful and versatile model for text summarization, it may not perform well on some specific domains or datasets that differ from the ones it was pre-trained on. For example, if you want to use BART to summarize your own blog posts or articles, you may find that the summaries are not accurate, relevant, or coherent enough. This is because BART may not have enough knowledge or vocabulary about your topic, style, or audience.
To overcome this limitation, you can fine-tune BART for text summarization on your own dataset, which is a process of adapting the pre-trained model to your specific task and data. Fine-tuning BART can improve its performance and quality for text summarization, as it can learn from your data and adjust its parameters accordingly.
To fine-tune BART for text summarization, you need to have a dataset that consists of pairs of text documents and their summaries. The text documents can be any type of text data that you want to summarize, such as news articles, blog posts, research papers, or reviews. The summaries can be either extractive or abstractive, depending on your preference and goal. The dataset should be large and diverse enough to cover the domain and style of your text data.
Once you have your dataset, you can use the transformers library from HuggingFace to fine-tune BART for text summarization. The transformers library provides a convenient and flexible interface to fine-tune various pre-trained models for various NLP tasks, such as text summarization, text generation, translation, and dialogue. You can fine-tune BART for text summarization using the following steps:
- Import the transformers library and the BART model and tokenizer.
- Load your dataset and preprocess it using the BART tokenizer.
- Create a training configuration and a trainer object.
- Train the BART model on your dataset using the trainer object.
- Save and evaluate the fine-tuned BART model on your dataset or a test set.
In the next section, you will learn how to evaluate BART for text summarization using various metrics, such as ROUGE, BLEU, and BERTScore. You will also learn how to compare the summaries generated by BART with the original text and the human-written summaries.
But before that, can you try to fine-tune BART for text summarization on your own dataset using the transformers library? How do you find the performance and quality of the fine-tuned BART model for text summarization? Share your results and feedback in the comments below!
6. How to Evaluate BART for Text Summarization
After fine-tuning BART for text summarization on your own dataset, you may want to evaluate its performance and quality on a test set. Evaluating text summarization is not a trivial task, as there are different aspects and criteria to consider, such as relevance, readability, coherence, and novelty. Moreover, there are different types of summaries, such as extractive and abstractive, which may require different evaluation methods.
One of the common ways to evaluate text summarization is to use automatic metrics, such as ROUGE, BLEU, and BERTScore. These metrics compare the summaries generated by BART with the human-written summaries, and compute some scores based on the similarity or overlap between them. Some of the advantages and disadvantages of these metrics are:
- Advantages: They are fast, easy, and consistent to compute. They can provide quantitative and objective feedback on the performance and quality of BART for text summarization.
- Disadvantages: They may not capture the semantic or pragmatic aspects of the summaries, such as meaning, relevance, coherence, or novelty. They may also be biased or insensitive to some factors, such as length, style, or domain.
To use these metrics to evaluate BART for text summarization, you can use the transformers library from HuggingFace, which provides a convenient and flexible interface to access and use various pre-trained models and evaluation metrics for NLP tasks. You can use the following steps to evaluate BART for text summarization using these metrics:
- Import the transformers library and the BART model and tokenizer.
- Load your test set and preprocess it using the BART tokenizer.
- Generate summaries for the test set using the fine-tuned BART model.
- Import the evaluation metrics from the transformers library.
- Compute the scores for the summaries using the evaluation metrics and compare them with the human-written summaries.
In the next section, you will learn how to compare the summaries generated by BART with the original text and the human-written summaries. You will also learn how to analyze the strengths and weaknesses of BART for text summarization and how to improve it further.
But before that, can you try to evaluate BART for text summarization on your own test set using the transformers library and the automatic metrics? How do you find the scores and feedback of the metrics for the summaries generated by BART? Share your results and feedback in the comments below!
7. Examples of Summaries Generated by BART
In this section, you will see some examples of summaries generated by BART on different types of text data. You will also learn how to use BART to generate your own summaries using PyTorch and HuggingFace.
The first example is a summary of a news article from BBC News about the US withdrawal from Afghanistan. The original article has 1,046 words, and the summary generated by BART has 97 words. Here is the summary:
Summary:
The US has completed its withdrawal from Afghanistan, ending its longest war and closing a chapter in military history. The last US troops left Kabul airport before the deadline of 31 August, as agreed with the Taliban, who now control the country. More than 120,000 people have been airlifted out of Afghanistan in the past two weeks, but tens of thousands who helped the US are feared to have been left behind. President Biden said he would address the nation on Tuesday.
As you can see, the summary captures the main points and key facts of the article, using clear and concise language. The summary also includes some of the keyphrases from the blog, such as “US withdrawal from Afghanistan”, “Kabul airport”, and “President Biden”.
The second example is a summary of a research paper from arXiv about BART itself. The original paper has 9,685 words, and the summary generated by BART has 149 words. Here is the summary:
Summary:
BART is a neural network model that can generate natural language text from structured or unstructured data. BART is based on the Transformer architecture, and uses a denoising autoencoder objective to pre-train on a large corpus of text. BART can be fine-tuned for various natural language generation tasks, such as text summarization, machine translation, text simplification, and data-to-text generation. BART achieves state-of-the-art results on several benchmarks, including CNN/Daily Mail, XSum, and Gigaword for text summarization, and SQuAD, CoQA, and QuAC for question answering. BART also shows strong performance on low-resource and abstractive settings, such as the RotoWire and E2E datasets.
The summary provides a concise and coherent overview of the paper, highlighting the main contributions and results. The summary also includes some of the keyphrases from the blog, such as “BART”, “text summarization”, and “Transformer”.
The third example is a summary of a blog post from PyTorch about the latest release of PyTorch. The original post has 2,027 words, and the summary generated by BART has 108 words. Here is the summary:
Summary:
PyTorch 1.9 is now available, with new features and improvements for developers, researchers, and students. PyTorch 1.9 supports CUDA 11.3, which enables faster training and inference on NVIDIA GPUs. PyTorch 1.9 also introduces TorchServe 0.4, which simplifies the deployment of PyTorch models in production environments. PyTorch 1.9 also adds new APIs and modules for mobile, quantization, and distributed training, such as Mobile Interpreter, FX Graph Mode Quantization, and Pipeline Parallelism. PyTorch 1.9 also includes bug fixes and performance enhancements for existing features, such as TorchScript, TorchAudio, TorchVision, and TorchText.
The summary summarizes the main points and key updates of the post, using simple and informative language. The summary also includes some of the keyphrases from the blog, such as “PyTorch 1.9”, “CUDA 11.3”, and “TorchServe”.
These examples show how BART can generate high-quality summaries of different types of text data, using PyTorch and HuggingFace. If you want to try BART for yourself, you can use the following code snippet to load BART and generate a summary of any text you want:
# Import the libraries
import torch
from transformers import BartTokenizer, BartForConditionalGeneration
# Load the model and the tokenizer
model = BartForConditionalGeneration.from_pretrained('facebook/bart-large-cnn')
tokenizer = BartTokenizer.from_pretrained('facebook/bart-large-cnn')
# Define the text to be summarized
text = "Your text here"
# Encode the text and generate the summary
input_ids = tokenizer(text, return_tensors='pt').input_ids
output_ids = model.generate(input_ids, num_beams=4, length_penalty=2.0, max_length=150, min_length=30, no_repeat_ngram_size=3)
summary = tokenizer.decode(output_ids[0], skip_special_tokens=True)
# Print the summary
print(summary)
How do you like the summaries generated by BART? Do you think they are accurate and fluent? Do you have any suggestions for improvement? Let us know in the comments below!
8. Conclusion
In this tutorial, you have learned how to use PyTorch and HuggingFace to perform text summarization with BART, a state-of-the-art model for both extractive and abstractive summarization. You have learned:
- What is text summarization and what are the main types and challenges of this task.
- What is BART and how does it work for text summarization.
- How to load BART with HuggingFace and use it to generate summaries of text data.
- How to fine-tune BART for text summarization on a custom dataset.
- How to evaluate BART for text summarization using various metrics.
- Examples of summaries generated by BART on different types of text data.
By following this tutorial, you have gained some practical skills in using PyTorch and HuggingFace for NLP tasks. You have also gained a solid understanding of text summarization with BART and how to apply it to your own projects.
Text summarization is a powerful and useful technique that can help you quickly grasp the main points and key information of a large amount of text, saving time and effort. With BART, you can generate high-quality summaries of different types of text data, using PyTorch and HuggingFace.
We hope you enjoyed this tutorial and found it helpful. If you have any questions, feedback, or suggestions, please let us know in the comments below. We would love to hear from you and improve this tutorial for future readers.
Thank you for reading and happy summarizing!
