《AI文本摘要工具实战：用BART模型处理长文档》

BART（Bidirectional and Auto-Regressive Transformers）是Facebook提出的预训练模型，结合双向编码和自回归解码结构，擅长文本生成任务（如摘要、翻译）。其优势在于处理长文档时能捕捉上下文依赖关系，生成流畅且准确的摘要。num_beams：束搜索数量，影响生成质量与速度。max_length：限制摘要长度。

barpiici3322

392人浏览 · 2025-09-06 03:48:19

barpiici3322 · 2025-09-06 03:48:19 发布

BART模型简介

BART（Bidirectional and Auto-Regressive Transformers）是Facebook提出的预训练模型，结合双向编码和自回归解码结构，擅长文本生成任务（如摘要、翻译）。其优势在于处理长文档时能捕捉上下文依赖关系，生成流畅且准确的摘要。

环境配置

安装必要的库（Python 3.7+）：

pip install transformers torch sentencepiece

加载预训练模型（以facebook/bart-large-cnn为例）：

from transformers import BartTokenizer, BartForConditionalGeneration
tokenizer = BartTokenizer.from_pretrained("facebook/bart-large-cnn")
model = BartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn")

长文档预处理

BART模型对输入长度有限制（通常1024 Token）。处理长文档时需分段或截断：

分段处理：将文档按段落分割，逐段生成摘要后合并。
关键句提取：先用TF-IDF或TextRank提取核心句子，再输入BART。

示例代码（分段处理）：

def split_text(text, max_length=1000):
    return [text[i:i+max_length] for i in range(0, len(text), max_length)]

生成摘要

输入文本需编码为Token，并生成摘要：

def summarize(text):
    inputs = tokenizer([text], max_length=1024, truncation=True, return_tensors="pt")
    summary_ids = model.generate(inputs["input_ids"], num_beams=4, max_length=200, early_stopping=True)
    return tokenizer.decode(summary_ids[0], skip_special_tokens=True)

参数说明：

num_beams：束搜索数量，影响生成质量与速度。
max_length：限制摘要长度。

后处理优化

冗余过滤：使用NLTK移除重复短语。
连贯性检查：通过ROUGE分数评估摘要与原文的贴合度。

示例完整流程

document = "长文档内容..."  # 替换为实际文本
segments = split_text(document)
summaries = [summarize(seg) for seg in segments]
final_summary = " ".join(summaries)

性能调优建议

使用fp16加速推理：model.half()。
尝试不同预训练变体（如bart-large-xsum更侧重抽象摘要）。
调整temperature参数控制生成多样性。

常见问题

显存不足：减小batch_size或启用梯度检查点。
摘要过长：降低max_length或增加length_penalty权重。

通过以上方法可高效处理长文档摘要任务，平衡生成质量与计算资源消耗。

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