Bart-Base-Story-Generation

Bart-Base-Story-Generation

A fine-tuned model for transforming research paper summaries into engaging short stories. This is the second stage of a two-step Research Paper Simplifier pipeline, built on top of harsharajkumar273/Bart-Base-Summarization.

Model Description

This model takes a summary of a research paper and generates an immersive, narrative-style short story that conveys the same ideas in an accessible way for a general audience.

Pipeline

Research Paper ──► [Bart-Base-Summarization] ──► Summary ──► [Bart-Base-Story-Generation] ──► Story

Training Details

Usage

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

# Stage 1: Summarize the paper
sum_tokenizer = AutoTokenizer.from_pretrained("harsharajkumar273/Bart-Base-Summarization")
sum_model = AutoModelForSeq2SeqLM.from_pretrained("harsharajkumar273/Bart-Base-Summarization")

paper_text = "Your research paper text here..."
word_count = len(paper_text.split())
sum_prompt = f"Summarize this part of the research paper to less than {word_count // 10} words:\n{paper_text}"
sum_inputs = sum_tokenizer(sum_prompt, return_tensors="pt", max_length=1024, truncation=True)
sum_outputs = sum_model.generate(**sum_inputs, max_length=128, num_beams=4)
summary = sum_tokenizer.decode(sum_outputs[0], skip_special_tokens=True)

# Stage 2: Generate a story from the summary
story_tokenizer = AutoTokenizer.from_pretrained("harsharajkumar273/Bart-Base-Story-Generation")
story_model = AutoModelForSeq2SeqLM.from_pretrained("harsharajkumar273/Bart-Base-Story-Generation")

story_inputs = story_tokenizer(summary, return_tensors="pt", max_length=512, truncation=True)
story_outputs = story_model.generate(**story_inputs, max_length=256, num_beams=4)
story = story_tokenizer.decode(story_outputs[0], skip_special_tokens=True)
print(story)

Evaluation Metrics

Evaluated using BERTScore and SBERTScore (semantic similarity via sentence-transformers) on a held-out 10% test split.

Related Models

• harsharajkumar273/Bart-Base-Summarization — previous stage
• harsharajkumar273/T5-Base-Story-Generation
• harsharajkumar273/ProphetNet-Large-Story-Generation