text-summarizer

$2a

• 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)