Long document summarization strategies in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load a pre-trained summarization model using Hugging Face Transformers.

NLP

from transformers import pipeline
summarizer = pipeline('summarization', model='[1]')

Drag options to blanks, or click blank then click option'

Agpt2

Broberta-base

Cfacebook/bart-large-cnn

Dbert-base-uncased

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing a model not designed for summarization like 'bert-base-uncased'.

Using a language model like 'gpt2' which is not fine-tuned for summarization.

The facebook/bart-large-cnn model is a popular pre-trained model for summarization tasks.

2fill in blank

medium

Complete the code to split a long document into chunks of 500 tokens for summarization.

NLP

def chunk_text(text, chunk_size=500):
    tokens = text.split()
    return [tokens[i:i+[1]] for i in range(0, len(tokens), [1])]

Drag options to blanks, or click blank then click option'

A500

B250

C1000

D50

Attempts:

3 left

💡 Hint

Common Mistakes

Using different numbers for slicing and step size causing uneven chunks.

Choosing chunk sizes too small or too large for model input limits.

We split the text into chunks of 500 tokens to manage long documents effectively.

3fill in blank

hard

Fix the error in the code that summarizes each chunk and combines the results.

NLP

def summarize_long_text(text, summarizer):
    chunks = chunk_text(text, 500)
    summaries = []
    for chunk in chunks:
        summary = summarizer(' '.join(chunk), max_length=150, min_length=40, do_sample=False)[[1]]['summary_text']
        summaries.append(summary)
    return ' '.join(summaries)

Drag options to blanks, or click blank then click option'

A0

Btext

Csummary

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Using index 1 which causes an index error.

Trying to access 'summary' or 'text' keys which do not exist.

The summarizer returns a list of dictionaries; the first element at index 0 contains the summary text.

4fill in blank

hard

Fill both blanks to create a function that uses a sliding window to summarize overlapping chunks.

NLP

def sliding_window_chunks(text, window_size=500, step_size=[1]):
    tokens = text.split()
    return [' '.join(tokens[i:i+window_size]) for i in range(0, len(tokens), [2]) if i + window_size <= len(tokens)]

Drag options to blanks, or click blank then click option'

A250

B500

C100

D50

Attempts:

3 left

💡 Hint

Common Mistakes

Using step size equal to window size, which causes no overlap.

Using step size larger than window size, which is invalid.

Using a step size of 250 creates overlapping chunks with a sliding window approach.

5fill in blank

hard

Fill all three blanks to build a pipeline that summarizes a long document by chunking, summarizing, and then summarizing the combined summary.

NLP

def hierarchical_summarization(text, summarizer):
    chunks = chunk_text(text, [1])
    summaries = [summarizer(' '.join(chunk), max_length=150, min_length=40, do_sample=False)[[2]]['summary_text'] for chunk in chunks]
    combined_summary = ' '.join(summaries)
    final_summary = summarizer(combined_summary, max_length=200, min_length=50, do_sample=False)[[3]]['summary_text']
    return final_summary

Drag options to blanks, or click blank then click option'

A500

B0

C1

D250

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong indices like 1 causing errors.

Using inconsistent chunk sizes.

Chunks of 500 tokens are summarized first. The summarizer output is accessed at index 0 for both steps.

Practice

1. Which of the following best describes the main goal of long document summarization?

easy

A. To count the number of words in a document

B. To translate text into another language

C. To make large texts shorter and easier to understand

D. To generate random text from a document

Solution

1. Step 1: Understand the purpose of summarization

   Summarization aims to reduce the length of a text while keeping important information.

2. Step 2: Match the goal with the options

   To make large texts shorter and easier to understand clearly states making large texts shorter and easier to understand, which matches the goal.

3. Final Answer:

   To make large texts shorter and easier to understand -> Option C

4. Quick Check:

   Summarization = shorten and simplify [OK]

Hint: Summarization means making text shorter and clear [OK]

Common Mistakes:

• Confusing summarization with translation
• Thinking summarization generates new unrelated text
• Believing summarization only counts words

2. Which of the following is a correct way to split a long document for summarization?

easy

A. Divide the document into smaller chunks based on paragraphs or sections

B. Split the document into random sentences without order

C. Use only the first sentence of the document as the summary

D. Combine all sentences into one long string without breaks

Solution

1. Step 1: Identify proper splitting methods

   Effective splitting uses logical boundaries like paragraphs or sections to keep context.

2. Step 2: Evaluate options for splitting

   Divide the document into smaller chunks based on paragraphs or sections divides by paragraphs or sections, preserving meaning and context, unlike random or no splitting.

3. Final Answer:

   Divide the document into smaller chunks based on paragraphs or sections -> Option A

4. Quick Check:

   Logical splitting = paragraphs/sections [OK]

Hint: Split by paragraphs or sections, not random sentences [OK]

Common Mistakes:

• Splitting randomly breaking context
• Using only first sentence as summary
• Merging all text without breaks

3. Given the following Python pseudocode for summarizing a long document:

chunks = split_document(document, size=500)
summaries = []
for chunk in chunks:
    summary = model.summarize(chunk)
    summaries.append(summary)
final_summary = model.summarize(' '.join(summaries))
print(final_summary)

What is the main purpose of the last line model.summarize(' '.join(summaries))?

medium

A. To summarize the entire document at once

B. To combine and summarize the smaller summaries into one final summary

C. To split the document into chunks

D. To print the original document

Solution

1. Step 1: Understand the code flow

   The code splits the document, summarizes each chunk, then combines those summaries.

2. Step 2: Analyze the last line's function

   The last line summarizes the combined smaller summaries to create one final summary.

3. Final Answer:

   To combine and summarize the smaller summaries into one final summary -> Option B

4. Quick Check:

   Final summary = summarize combined chunk summaries [OK]

Hint: Last step merges chunk summaries into one summary [OK]

Common Mistakes:

• Thinking it summarizes the whole document at once
• Confusing splitting with summarizing
• Assuming it prints original text

4. Consider this code snippet for summarizing a long document:

def summarize_long_doc(doc):
    chunks = doc.split('.')
    summaries = []
    for chunk in chunks:
        summary = model.summarize(chunk)
        summaries.append(summary)
    return ' '.join(summaries)

final_summary = summarize_long_doc(long_text)
print(final_summary)

What is the main issue with this approach?

medium

A. The summaries list is never used

B. The model.summarize function is called only once

C. The function returns the original document instead of a summary

D. Splitting by '.' may break sentences improperly, losing context

Solution

1. Step 1: Examine the splitting method

   Splitting by '.' breaks text at every period, which can split sentences incorrectly.

2. Step 2: Understand the impact on summarization

   Improper splitting can cause loss of sentence meaning and context, hurting summary quality.

3. Final Answer:

   Splitting by '.' may break sentences improperly, losing context -> Option D

4. Quick Check:

   Improper splitting = lost context [OK]

Hint: Splitting by '.' can break sentences badly [OK]

Common Mistakes:

• Thinking summarize is called once (it's called multiple times)
• Believing summaries list is unused
• Assuming function returns original text

5. You have a very long legal document that exceeds your model's input limit. Which strategy best combines summarization methods to handle this?

hard

A. Use extractive summarization on chunks, then abstractive summarization on combined chunk summaries

B. Summarize only the first 1000 words and ignore the rest

C. Apply abstractive summarization directly on the entire document without splitting

D. Translate the document to another language before summarizing

Solution

1. Step 1: Understand model input limits

   Long documents must be split because models have maximum input sizes.

2. Step 2: Combine summarization methods effectively

   Extractive summarization on chunks keeps key info, then abstractive summarization on those summaries creates a concise final summary.

3. Step 3: Evaluate options

   Use extractive summarization on chunks, then abstractive summarization on combined chunk summaries uses a hybrid approach suitable for very long documents, unlike ignoring parts or direct abstractive summarization which may fail.

4. Final Answer:

   Use extractive summarization on chunks, then abstractive summarization on combined chunk summaries -> Option A

5. Quick Check:

   Hybrid method = extractive + abstractive [OK]

Hint: Combine extractive then abstractive for very long texts [OK]

Common Mistakes:

• Ignoring parts of the document
• Trying abstractive summarization on too long input
• Confusing translation with summarization