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Quiz Part III: Topic Modeling and Text Summarization
Chapter 8: Text Summarization
- What is the main difference between extractive and abstractive summarization?
- A) Extractive summarization generates new sentences, while abstractive summarization selects existing sentences.
- B) Extractive summarization selects key sentences from the original text, while abstractive summarization generates new sentences.
- C) Extractive summarization is more complex than abstractive summarization.
- D) Extractive summarization requires more training data than abstractive summarization.
- Which algorithm is used in the TextRank method for extractive summarization?
- A) Singular Value Decomposition (SVD)
- B) PageRank
- C) K-means clustering
- D) Hidden Markov Model (HMM)
- Which model did we use for abstractive summarization in the exercises?
- A) Word2Vec
- B) BERT
- C) BART
- D) LSTM
- What is a key advantage of abstractive summarization over extractive summarization?
- A) Abstractive summarization is simpler to implement
- B) Abstractive summarization produces more coherent and readable summaries
- C) Abstractive summarization requires less computational power
- D) Abstractive summarization always produces shorter summaries
- Which library provides the pre-trained models BART and T5 for abstractive summarization?
- A) NLTK
- B) Gensim
- C) TensorFlow
- D) Hugging Face Transformers
Chapter 8: Text Summarization
- What is the main difference between extractive and abstractive summarization?
- A) Extractive summarization generates new sentences, while abstractive summarization selects existing sentences.
- B) Extractive summarization selects key sentences from the original text, while abstractive summarization generates new sentences.
- C) Extractive summarization is more complex than abstractive summarization.
- D) Extractive summarization requires more training data than abstractive summarization.
- Which algorithm is used in the TextRank method for extractive summarization?
- A) Singular Value Decomposition (SVD)
- B) PageRank
- C) K-means clustering
- D) Hidden Markov Model (HMM)
- Which model did we use for abstractive summarization in the exercises?
- A) Word2Vec
- B) BERT
- C) BART
- D) LSTM
- What is a key advantage of abstractive summarization over extractive summarization?
- A) Abstractive summarization is simpler to implement
- B) Abstractive summarization produces more coherent and readable summaries
- C) Abstractive summarization requires less computational power
- D) Abstractive summarization always produces shorter summaries
- Which library provides the pre-trained models BART and T5 for abstractive summarization?
- A) NLTK
- B) Gensim
- C) TensorFlow
- D) Hugging Face Transformers
Chapter 8: Text Summarization
- What is the main difference between extractive and abstractive summarization?
- A) Extractive summarization generates new sentences, while abstractive summarization selects existing sentences.
- B) Extractive summarization selects key sentences from the original text, while abstractive summarization generates new sentences.
- C) Extractive summarization is more complex than abstractive summarization.
- D) Extractive summarization requires more training data than abstractive summarization.
- Which algorithm is used in the TextRank method for extractive summarization?
- A) Singular Value Decomposition (SVD)
- B) PageRank
- C) K-means clustering
- D) Hidden Markov Model (HMM)
- Which model did we use for abstractive summarization in the exercises?
- A) Word2Vec
- B) BERT
- C) BART
- D) LSTM
- What is a key advantage of abstractive summarization over extractive summarization?
- A) Abstractive summarization is simpler to implement
- B) Abstractive summarization produces more coherent and readable summaries
- C) Abstractive summarization requires less computational power
- D) Abstractive summarization always produces shorter summaries
- Which library provides the pre-trained models BART and T5 for abstractive summarization?
- A) NLTK
- B) Gensim
- C) TensorFlow
- D) Hugging Face Transformers
Chapter 8: Text Summarization
- What is the main difference between extractive and abstractive summarization?
- A) Extractive summarization generates new sentences, while abstractive summarization selects existing sentences.
- B) Extractive summarization selects key sentences from the original text, while abstractive summarization generates new sentences.
- C) Extractive summarization is more complex than abstractive summarization.
- D) Extractive summarization requires more training data than abstractive summarization.
- Which algorithm is used in the TextRank method for extractive summarization?
- A) Singular Value Decomposition (SVD)
- B) PageRank
- C) K-means clustering
- D) Hidden Markov Model (HMM)
- Which model did we use for abstractive summarization in the exercises?
- A) Word2Vec
- B) BERT
- C) BART
- D) LSTM
- What is a key advantage of abstractive summarization over extractive summarization?
- A) Abstractive summarization is simpler to implement
- B) Abstractive summarization produces more coherent and readable summaries
- C) Abstractive summarization requires less computational power
- D) Abstractive summarization always produces shorter summaries
- Which library provides the pre-trained models BART and T5 for abstractive summarization?
- A) NLTK
- B) Gensim
- C) TensorFlow
- D) Hugging Face Transformers