"Text summarization has come a long way from basic techniques like picking out keyword sentences. Early methods often struggled with capturing the nuance and flow of text. Today, fine-tuned transformer models offer a significant leap forward. These powerful models, trained on massive datasets and customized for summarization tasks, can generate summaries that are not only factually accurate but also coherent and readable. This allows users to quickly grasp the main points of lengthy texts, making information retrieval and comprehension more efficient."- Gemini 2024
Text summarization is a powerful natural language processing technique with wide-ranging applications across various industries and everyday life. It enables the quick extraction of key information from large volumes of text, saving time and improving efficiency.
Here are just a few ways we see summaries in use today:
POS tagging is a cornerstone of many NLP applications. It acts as the foundation for machines to grasp sentence structure. This clarifies word meaning (such as identifying "bat" as a noun or verb), improves feature extraction for tasks like sentiment analysis, and provides a foundation for building more complex NLP systems that can parse sentences or identify entities.
By providing a basic understanding of language structure, POS tagging empowers machines to process and analyze text more effectively.
Text summarization can be achieved with simple techniques, like extractive summarization, which has long served as a reliable method to condense lengthy texts. Such methods often involve selecting key sentences based on factors like word frequency or position.
In this example, we create a simple text summarizing script that uses POS tagging to identify and extract important sentences from a given text.
Overview of steps in script
import nltk from nltk.tokenize import sent_tokenize, word_tokenize from nltk.corpus import stopwords from nltk.probability import FreqDist # Download required NLTK data nltk.download('punkt') nltk.download('stopwords') nltk.download('averaged_perceptron_tagger') def pos_summarizer(text, num_sentences=3): # Tokenize words and remove stopwords words = word_tokenize(text.lower()) stop_words = set(stopwords.words('english')) words = [word for word in words if word not in stop_words] # Get POS tags pos_tags = nltk.pos_tag(words) # Extract nouns and verbs and calculate frequency key_words = [word for word, pos in pos_tags if pos.startswith('NN') or pos.startswith('VB')] word_freq = FreqDist(key_words) # Score sentences based on frequency of key words sentences = sent_tokenize(text) scores = {} for i, sentence in enumerate(sentences): for word in word_tokenize(sentence.lower()): if word in word_freq: if i in scores: scores[i] += word_freq[word] else: scores[i] = word_freq[word] # Get the [num_sentences] top scoring sentences and order by appearance in text top_sentences = sorted(scores, key=scores.get, reverse=True)[:num_sentences] summary = ' '.join([sentences[i] for i in sorted(top_sentences)]) return summary # Example usage from io import BytesIO from PyPDF2 import PdfReader # Download an article PDF from the web and extract the text # https://techxplore.com/news/2024-07-brain-artificial-dendritic-neural-circuit.html text = '' with open('2024-07-brain-artificial-dendritic-neural-circuit.pdf', 'rb') as f: pdf_content = BytesIO(f.read()) reader = PdfReader(pdf_content) for page in reader.pages: text += page.extract_text() # Preprocess text text = text.strip().replace('\n', ' ') # Get summary summary = pos_summarizer(text) print(summary)
This outputs the following summary, which is not too bad!
While simpler methods are efficient, they might lack nuance. LLMs, on the other hand, have the potential to generate more engaging and informative summaries, but raise concerns about computational cost and potential biases. This exploration compares LLM outputs from a general model, and the same model, fine-tuned for the task.
This script compares the output of a text-to-text transformer model (google-t5/t5-small) with a related model fine-tuned for the task (koppolusameer/t5-finetuned-summarization-samsum).
from transformers import T5Tokenizer, T5ForConditionalGeneration def summarize_text(text, model_name, max_length=150): # Load pre-trained model and tokenizer tokenizer = T5Tokenizer.from_pretrained(model_name) model = T5ForConditionalGeneration.from_pretrained(model_name) # Build prompt prompt = "summarize: " + text # Tokenize the text tokens = tokenizer.encode(prompt, return_tensors='pt', max_length=512, truncation=True) # Generate and decode summaries summaries = model.generate( tokens, num_beams=4, no_repeat_ngram_size=2, min_length=30, max_length=max_length, early_stopping=True ) summary = tokenizer.decode(summaries[0], skip_special_tokens=True) return summary # Example usage from io import BytesIO from PyPDF2 import PdfReader # Download an article PDF from the web and extract the text # https://techxplore.com/news/2024-07-brain-artificial-dendritic-neural-circuit.html text = '' with open('2024-07-brain-artificial-dendritic-neural-circuit.pdf', 'rb') as f: pdf_content = BytesIO(f.read()) reader = PdfReader(pdf_content) for page in reader.pages: text += page.extract_text() # Preprocess text text = text.strip().replace('\n', ' ') # Get summary for each model to compare base_model = 't5-small' fine_tuned = 'koppolusameer/t5-finetuned-summarization-samsum' summary = summarize_text(text, base_model) print("\nBase model\n\n", summary) summary = summarize_text(text, fine_tuned) print("\nFine tuned model\n\n", summary)
In the output from this script, we see the summarization getting progressively better
In our comparison of LLMs to simpler techniques, one can assess the trade-off between efficiency and quality in text summarization. Considering the resource cost, execution time, and potential for bias, is the improvement in summarizing worth the cost.
Ultimately, the decision comes down to necessity and resources, both highly reliant on the particular use case and environment.