Text Summarizer

Create concise summaries from long text documents using extractive summarization. Identifies and extracts the most important sentences while preserving meaning.

Quick Start 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)

Features Extractive Summarization: Selects key sentences from original text Length Control: By ratio, sentence count, or word count Multiple Algorithms: TextRank, LSA, frequency-based Key Points: Extract bullet-point summaries Batch Processing: Summarize multiple documents Preserve Structure: Maintains sentence order option API Reference Initialization summarizer = TextSummarizer( method="textrank", # textrank, lsa, frequency language="english" )

Summarization

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)

Key Points Extraction

Get bullet points

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

Batch Processing

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)

Options

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 Usage

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 Arguments Argument Description Default --input Input file path Required --output Output file path stdout --input-dir Directory of files - --output-dir Output directory - --ratio Summary ratio (0.0-1.0) 0.2 --sentences Number of sentences - --words Maximum words - --points Extract N key points - --method Algorithm to use textrank --preserve-order Keep sentence order False Examples News Article Summary 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}")

Research Paper Abstract summarizer = TextSummarizer(method="lsa")

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

Create abstract-length summary

abstract = summarizer.summarize(paper, max_words=250) print(abstract)

Meeting Notes Summary 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}")

Batch Document Summarization 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}")

Algorithm Comparison Algorithm Speed Quality Best For TextRank Medium High General text LSA Fast Good Technical docs Frequency Fast Medium Quick summaries Dependencies nltk>=3.8.0 numpy>=1.24.0 scikit-learn>=1.2.0

Limitations Extractive only (doesn't paraphrase or generate new text) Works best with well-structured text (paragraphs, clear sentences) Very short texts may not summarize well Doesn't understand context deeply (may miss nuance)