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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)
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