Comparing Text Summarization Models: GPT vs BERT vs LLMs

In this new era of artificial intelligence (AI), large language models (LLMS) have emerged as a transformative force that has redefined how humans interact with machines. It has bridged the gap between how even people with less or no technical knowledge make use of the most modern technology by being a platform for them to interact with machines in common and natural human language, without the need to learn coding or programming.

At their core, LLMs are sophisticated machine learning models that are trained on vast datasets of texts. They use this training to understand, comprehend and in turn produce text that closely resembles human-like language, and use this to answer questions, engage in conversations, write essays, and even write codes. LLMs are built on neural networks in AI architecture called transformers that can manipulate sequences of words and capture patterns in text.

These LLMs are the basis of AI text summarization models, which are used to generate summaries of large texts like legal documents, contracts, academic research papers and others. There are various types of LLMs based on natural language processing (NLP) used for AI summarization with GPT, BERT, BART and RoBERTa being some of the most popular ones.

In this post, we’ll explore the difference between the above said AI text summarization models.

But before that let’s look at the different types of LLM architectures.

Types of LLM Architectures Explained

The following are the three main LLM architectures:

• • Decoder-only Models (Autoregressive): These models use the preceding words in a sentence to predict the next word, and this makes them best LLM architecture for conversational AI, and text generation.

Examples: GPT-3, GPT-4, LLaMA

• • Encoder-only Models (Masked Language Models): These models are good for text classification and understanding language better as these can fill in missing words within a sentence.

Examples: BERT, RoBERTA

• • Encoder-Decoder Models (Seq2Seq): With their capability to convert one sequence of text into another, they are ideal for summarization and translation tasks.

Examples: BART, T5

GPT vs BERT vs LLMs explained

BERT

Introduced by Google in 2018, BERT is an acronym for Bidirectional Encoder Representations from Transformers. It is built on the encoder-only architecture and refers to the words before and after to understand the context of words in a sentence.

Key Features

• Bidirectional Attention: Unlike other models that process text from left to right, or right to left, BERT looks at both directions to understand the context of the words in a sentence. Consider the sentence: “The eagle hovered over the _____ on a sunny evening.” BERT can fill that blank with the word ‘horizon’ by referring to the words before and after the blank.
• Masked Language Model (MLM): Considering the previous example, BERT achieves this by training itself by hiding (or masking) some words in a sentence and then trying to predict them with the reference of the words before and after them. This makes it capable of understanding the context of a word within a sentence.

Applications of BERT

• Question Answering: BERT can rightly find answers to specific questions after it is given a text as input.
• Text Classification: BERT is very useful in identifying and classifying texts, such as spotting spams in emails.
• Named Entity Recognition (NER): Specific entities like names, dates, and locations in a text can be correctly identified by BERT.

GPT
Though a transformer-based model like BERT, GPT (Generative Pretrained Transformer) has a slightly different approach for text recognition. GPT is a decoder-only model introduced by OpenAI in 2018 mainly to deal with text generation, making it an ideal tool for conversations, writing articles and text completion tasks.

Key Features

• Unidirectional Attention: As a decoder-only model, GPT only looks at the words before a given word in a sentence. For example, in the sentence “The cat sat on the ___,” GPT would only consider the words before the blank, predicting the next word in sequence.
• Autoregressive Language Model: Considering the previous example: “The cat sat on the ___,” GPT is trained to predict the next word in a sentence as “mat” by pre-training it on a massive amount of text data and fine-tuning it for specific tasks, enabling it to generate human-like text.

Applications of GPT

• Text Summarization: GPT is an ideal tool for shortening lengthy texts into concise and relevant summaries.
• Text Generation: GPT is capable of generating creative content like stories, blog posts, or even code.
• Conversational Agents: GPT-powered chatbots and virtual assistants can respond to queries in a natural manner.

BART

Being an encoder-decoder model, Bidirectional and Auto- Regressive Transformers (BART), proposed by Facebook in 2019, is a combination of BERT and GPT. What it means is that it first processes text in a bidirectional manner to understand the context of the text like BERT, and then generates a text the summary in a left to right (autoregressive unidirectional) manner like GPT does. BART is specifically designed for text generation.

Key Features

• Multi-head Self-attention and Cross-attention: The encoder used in BART is a stack of layers that consist of multi-head self-attention and feed-forward neural networks in AI that allows every word in the input sequence to attend to all other words, while the decoder within BART incorporates a cross-attention mechanism, enabling it to focus on the encoder’s output. This combination of self-attention that captures internal dependencies to the generated sequence and cross-attention allows the input sequence to give BART capabilities that are better than others in tasks like machine translation and text generation.

Applications of BART

• Text Generation and Summarization: BART combines the capabilities of BERT and GPT to effectively capture the dependencies between different parts of the input text. This approach makes BART capable of generating more coherent and contextually accurate text, making it suitable for a wide range of applications such as document summarization, language translation, and dialogue generation.
• Machine Translation: BART is effective in machine translation and combines bidirectional and autoregressive models to improve translation quality. Bidirectional models have the advantage of capturing the context from both the source and target languages, while autoregressive models generate translations one word at a time by conditioning on previously generated words. BART utilizes the Transformer architecture to learn contextual representations of words and generate high-quality translations.

RoBERTa

Robustly Optimized BERT Approach (RoBERTa) is a successor to BERT. It primarily improves on the capabilities of BERT by carefully optimizing the training hyperparameters for BERT. RoBERTa is designed to outperform BERT by making several changes and this adds to RoBERTa’s popularity among the AI/NLP community. RoBERTa literally uses the same architecture as BERT but it is only pre-trained with Masked Language Modeling (MLM) unlike BERT, which is also pre-trained with Next Sentence Prediction (NSP).

Key Features

• Training Enhancements: RoBERTa builds upon BERT’s foundation but with several key improvements.
• Training Objective Modification: RoBERTa omits NSP, focusing solely on MLM based on findings that NSP does not contribute significantly to performance improvements.
• Robust Training Procedures: RoBERTa’s robustness and accuracy are enhanced by dynamic masking (BERT uses static masking) and extended training.

Applications of RoBERTa

• Text Classification: RoBERTa is widely used for classifying text into categories like ‘Sentiment Analysis’, ‘Spam Detection’, and ‘Intent Classification’.
• Named Entity Recognition (NER): This involves identifying entities like names, dates, and locations in a text with accuracy. RoBERTa’s contextual understanding helps improve accuracy in complex and ambiguous contexts.
• Summarization: RoBERTa performs extractive summarization by selecting the most relevant sentences from long documents such as articles or reports. It’s ideal for producing concise overviews without generating new text.
• Domain-specific Text Mining: RoBERTa variants like Legal-RoBERTa and BioRoBERTa can be trained to extract texts for specific fields like Legal NLP and Biomedical NLP respectively.

Conclusion

There are many other LLMs that can be used for specific functions like DistilBERT, which is 40% smaller than the original BERT-base model, but is 60% faster, and retains 97% of its functionality; ALBERT (A Lite BERT), which introduces several techniques to reduce model size while maintaining performance; XLNet, which utilizes permutation-based language modelling to capture bidirectional context; ELECTRA (Efficiently Learning an Encoder that Classifies Token Replacements Accurately), which replaces a portion of the input tokens with plausible alternatives generated by another neural network called the “discriminator”; T5 (Text-to-Text Transfer Transformer), which adopts a unified “text-to-text” framework; and more but they aren’t one-size-fits-all.

The world of natural language processing (NLP) is ever evolving, and these models represent just the beginning. Understanding the differences between each can make choosing the right LLM for business applications easier for you, and this is where an experienced consultant like DeepKnit AI can help you. Consult with our experts to find out which would be the best fit for your business.