Natural Language Processing with Python Updated EditionChapter 112
Chapter 8: Text Summarization
Section 2 of 3-~ 2 min read-Synced from Cuantum content
1. 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.
1. 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)
1. Which model did we use for abstractive summarization in the exercises? - A) Word2Vec
- B) BERT
- C) BART
- D) LSTM
1. 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
1. Which library provides the pre-trained models BART and T5 for abstractive summarization? - A) NLTK
- B) Gensim
- C) TensorFlow
- D) Hugging Face Transformers