How to Select Embedding model for Summarization Task

Selecting the optimal embedding model is a crucial step when developing an automatic text summarization system. The embedding model transforms raw text into dense vector representations that capture semantic meaning. This vectorized representation of the text serves as the input to the summarization model. The effectiveness of the overall summarization pipeline hinges on using an embedding model that is well-suited to the specific attributes of the summarization task. When choosing an embedding technique, key factors to consider include the genre and linguistic properties of the documents being summarized, hardware constraints like memory and GPU availability, and characteristics of the reader for whom the summaries are intended. The ideal embedding model accurately encodes the semantic essence of the source text in a way that facilitates generating useful summaries. This article provides guidance on assessing embedding models and selecting the right one to match the needs of different summarization tasks. With careful embedding selection, impactful gains can be achieved in producing informative, concise summaries.

Understanding the Text and Objective

The first step in choosing an embedding model is to understand the nature of the text you want to summarize. Is it technical documentation, news articles, or academic papers? The language, terminology, and complexity of the text are crucial factors. If the text is highly specialized, like cybersecurity documents, you may need a domain-specific model trained on a similar corpus. This ensures that the model understands the jargon and can produce summaries that maintain the nuances of the original text.

Embedding Models: A Close Look

There are various types of word embeddings such as Word2Vec, GloVe, and FastText, as well as sentence or document embeddings like Doc2Vec, Sentence-BERT, and Universal Sentence Encoder. More recent transformer models like BERT, GPT-2, and RoBERTa offer even more powerful contextual embeddings.

1. Word2Vec and GloVe: These are classic models that capture semantic similarity but may not capture contextual nuances well. They are computationally less intensive and can be good for summarizing general text but may not be suitable for highly technical content.
2. Doc2Vec and Sentence Embeddings: These capture the semantics at the sentence or document level and are useful when sentence structures are complex and loaded with meaning. They are often used in extractive summarization.
3. Transformers like BERT, GPT-2, RoBERTa: These models capture deep contextual relationships and are excellent for tasks that require understanding of context, co-reference, and other linguistic nuances. However, they are computationally intensive.
4. Domain-Specific Models: In specialized fields like cybersecurity, models trained on domain-specific data can offer superior performance. These models can be fine-tuned versions of any of the above types, optimized for the particular vocabulary and idiomatic expressions of the field.

Extractive vs. Abstractive Summarization

Your choice also depends on whether you are performing extractive or abstractive summarization. Extractive models select whole sentences from the original text, while abstractive models generate new sentences. Sentence embeddings like Sentence-BERT are often used for extractive summarization, whereas transformer models are generally better for abstractive tasks due to their ability to generate coherent and contextually relevant text.

Computational Resources

The computational resources you have at your disposal are another important consideration. Transformer-based models, while powerful, require significant computational power and memory. If resource constraints are a concern, simpler models like Word2Vec or GloVe, or even quantized versions of transformer models, may be more appropriate.

Evaluation Metrics

Once you've chosen a model, it's critical to evaluate its performance using metrics like ROUGE for extractive summarization or BLEU for abstractive summarization. Custom evaluation metrics can also be designed to assess whether the model is meeting domain-specific requirements, such as the correct interpretation of technical terms in cybersecurity.

Fine-Tuning and Iterative Development

Even after initial selection, it's often necessary to fine-tune the model on a specific corpus to improve its performance. This involves training the model on a subset of your data and then adjusting its parameters based on performance metrics.

To summarize, choosing the right embedding model for a summarization task is a multifaceted decision that involves a deep understanding of the text corpus, the summarization objectives, and the technical constraints. Given that your audience is primarily technical and focused on cybersecurity, a domain-specific transformer-based model could be the most effective choice, provided computational resources are available. Always remember to evaluate the model rigorously using relevant metrics to ensure it meets the needs of your specific task.

We will give two examples with code snippet to help you to understand how to select an embedding model.

Summarizing Research Papers on Generative AI

1. Data Characteristics: Research papers are highly structured and dense with domain-specific terminology. Given this, it's safe to assume that these papers will have a lot of technical details and mathematical equations.
2. Embedding Model Choice: A transformer-based model like BERT or RoBERTa, fine-tuned on a corpus of AI research papers, would be ideal. These models excel at capturing contextual relationships and are adept at understanding complex sentence structures.
3. Summarization Strategy: Abstractive summarization would be more suitable as it allows for the condensation of complex ideas into shorter sentences, which is often necessary when summarizing research papers.
4. Concrete Steps:
5. First, tokenize the research paper into sentences or paragraphs.
6. Use the fine-tuned BERT or RoBERTa model to generate embeddings for each sentence or paragraph.
7. Implement a summarization algorithm (e.g., TextRank) that utilizes these embeddings to rank and select the most important sentences.
8. Finally, use the same transformer model for generating an abstractive summary from these selected sentences.

from transformers import BertTokenizer, BertModel

from sklearn.metrics.pairwise import cosine_similarity

from summarizer import TextRank

# Initialize BERT model and tokenizer

tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

model = BertModel.from_pretrained('bert-base-uncased')

# Tokenize and generate embeddings for the research paper

sentences = ["sentence1", "sentence2", ...]

embeddings = []

for sent in sentences:

tokens = tokenizer(sent, padding=True, truncation=True, return_tensors='pt')

with torch.no_grad():

output = model(**tokens)

embeddings.append(output.last_hidden_state.mean(dim=1))

# Use TextRank or similar algorithms for ranking sentences

textRank = TextRank(embeddings)

summary_indices = textRank.get_top_n_indices(n=5)