text-summarizer

文本摘要器

使用抽取式摘要从长篇文档中生成简洁摘要。识别并提取最重要的句子，同时保留原意。

快速入门

from scripts.text_summarizer import TextSummarizer

# Summarize text
summarizer = TextSummarizer()
summary = summarizer.summarize(long_text, ratio=0.2)  # 20% of original
print(summary)

# Summarize file
summary = summarizer.summarize_file("article.txt", num_sentences=5)

功能

• 抽取式摘要：从原文中选取关键句子
• 长度控制：可按比例、句子数量或词数控制
• 多种算法：TextRank、LSA、基于词频的算法
• 要点：提取项目符号形式的摘要
• 批量处理：为多个文档生成摘要
• 保留结构：提供保持句子顺序的选项

API 参考

初始化

summarizer = TextSummarizer(
    method="textrank",    # textrank, lsa, frequency
    language="english"
)

摘要生成

# By ratio (20% of original length)
summary = summarizer.summarize(text, ratio=0.2)

# By sentence count
summary = summarizer.summarize(text, num_sentences=5)

# By word count
summary = summarizer.summarize(text, max_words=100)

要点提取

# Get bullet points
points = summarizer.extract_key_points(text, num_points=5)
for point in points:
    print(f"• {point}")

批量处理

# Summarize multiple texts
texts = [text1, text2, text3]
summaries = summarizer.summarize_batch(texts, ratio=0.2)

# Summarize files in directory
summaries = summarizer.summarize_directory("./articles/", ratio=0.3)

选项

# Preserve original sentence order
summary = summarizer.summarize(text, preserve_order=True)

# Include title/first sentence
summary = summarizer.summarize(text, include_first=True)

# Minimum sentence length filter
summarizer.min_sentence_length = 10

CLI 用法

# Summarize text file
python text_summarizer.py --input article.txt --ratio 0.2

# Specific sentence count
python text_summarizer.py --input article.txt --sentences 5

# Extract key points
python text_summarizer.py --input article.txt --points 5

# Batch process
python text_summarizer.py --input-dir ./docs --output-dir ./summaries --ratio 0.3

# Output to file
python text_summarizer.py --input article.txt --output summary.txt --ratio 0.2

CLI 参数

参数	描述	默认值
--input	输入文件路径	必填
--output	输出文件路径	stdout
--input-dir	文件所在目录	-
--output-dir	输出目录	-
--ratio	摘要比例（0.0-1.0）	0.2
--sentences	句子数量	-
--words	最大词数	-
--points	提取 N 个要点	-
--method	要使用的算法	textrank
--preserve-order	保持句子顺序	False

示例

新闻文章摘要

summarizer = TextSummarizer()

article = """
[Long news article text...]
"""

# Get a 3-sentence summary
summary = summarizer.summarize(article, num_sentences=3)
print("Summary:")
print(summary)

# Get key points
points = summarizer.extract_key_points(article, num_points=5)
print("\nKey Points:")
for i, point in enumerate(points, 1):
    print(f"{i}. {point}")

研究论文摘要

summarizer = TextSummarizer(method="lsa")

paper = open("research_paper.txt").read()

# Create abstract-length summary
abstract = summarizer.summarize(paper, max_words=250)
print(abstract)

会议记录摘要

summarizer = TextSummarizer()

notes = """
Meeting started at 2pm. John presented Q3 results showing 15% growth.
Sarah raised concerns about supply chain delays affecting Q4 projections.
The team discussed mitigation strategies including dual-sourcing.
Budget allocation for marketing was approved at $50k.
Next steps include vendor outreach by Friday.
Follow-up meeting scheduled for next Tuesday.
"""

summary = summarizer.summarize(notes, num_sentences=3)
points = summarizer.extract_key_points(notes, num_points=4)

print("Summary:", summary)
print("\nAction Items:")
for point in points:
    print(f"• {point}")

批量文档摘要

summarizer = TextSummarizer()

import os
for filename in os.listdir("./documents"):
    if filename.endswith(".txt"):
        text = open(f"./documents/{filename}").read()
        summary = summarizer.summarize(text, ratio=0.2)

        with open(f"./summaries/{filename}", "w") as f:
            f.write(summary)

        print(f"Summarized: {filename}")

算法对比

算法	速度	质量	最适用场景
TextRank	中等	高	通用文本
LSA	快	良好	技术文档
Frequency	快	中等	快速生成摘要

依赖项

nltk>=3.8.0
numpy>=1.24.0
scikit-learn>=1.2.0

局限性

• 仅支持抽取式摘要（不会改写或生成新文本）
• 最适用于结构清晰的文本（段落和明确的句子）
• 过短的文本可能无法生成良好摘要
• 无法深入理解上下文（可能会遗漏细微差别）