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| # Imports | |
| # | |
| # 3rd-party modules | |
| from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM | |
| import nltk | |
| from nltk import sent_tokenize | |
| from collections import Counter | |
| # Download NLTK data | |
| nltk.download('punkt') | |
| # Load a pre-trained model and tokenizer for summarization | |
| model_name = "facebook/bart-large-cnn" # You can also use "t5-base" or another model | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForSeq2SeqLM.from_pretrained(model_name) | |
| # Summarization pipeline | |
| summarizer = pipeline("summarization", model=model, tokenizer=tokenizer) | |
| # Step 1: Specific Event Extraction | |
| def extract_events(text): | |
| """ | |
| Extract events from the input text. | |
| Here, sentences are considered as events. | |
| """ | |
| sentences = sent_tokenize(text) | |
| return sentences | |
| # Step 2: Event Abstraction and Generalization | |
| def abstract_events(events): | |
| """ | |
| Generalize the extracted events using a summarization model. | |
| Each event (sentence) is abstracted and summarized. | |
| """ | |
| abstracted_events = [summarizer(event, max_length=30, min_length=10, do_sample=False)[0]['summary_text'] for event | |
| in events] | |
| return abstracted_events | |
| # Step 3: Common Event Statistics | |
| def common_events(abstracted_events): | |
| """ | |
| Analyze the abstracted events to find out which events are most common. | |
| """ | |
| event_counter = Counter(abstracted_events) | |
| # Select the most common events (those that appear more than once) | |
| common_events = [event for event, count in event_counter.items() if count > 1] | |
| return common_events | |
| # Step 4: Summary Generation | |
| def generate_summary(common_events): | |
| """ | |
| Generate a concise summary from the most common events. | |
| """ | |
| combined_text = " ".join(common_events) | |
| summary = summarizer(combined_text, max_length=100, min_length=50, do_sample=False)[0]['summary_text'] | |
| return summary | |
| # Chain-of-Event Prompting Process | |
| def chain_of_event_prompting(texts): | |
| """ | |
| Full Chain-of-Event Prompting workflow: | |
| 1. Extract events from multiple texts. | |
| 2. Generalize and abstract the events. | |
| 3. Analyze the commonality of the events. | |
| 4. Generate a summary from the common events. | |
| """ | |
| all_events = [] | |
| for text in texts: | |
| events = extract_events(text) | |
| abstracted_events = abstract_events(events) | |
| all_events.extend(abstracted_events) | |
| common_events_list = common_events(all_events) | |
| summary = generate_summary(common_events_list) | |
| return summary | |
| # Example Usage | |
| if __name__ == "__main__": | |
| # Example input texts | |
| texts = [ | |
| "The company announced a new product line which will be launched next month.", | |
| "A new product line is being developed by the company, with a launch expected in the near future.", | |
| "Next month, the company plans to introduce a new series of products to the market." | |
| ] | |
| # Perform Chain-of-Event Prompting | |
| final_summary = chain_of_event_prompting(texts) | |
| print("Final Summary:", final_summary) | |