Natural Language Processing with Python Updated EditionChapter 112

Chapter 8: Text Summarization

Section 3 of 3-~ 12 min read-Synced from Cuantum content
  1. What is the main difference between extractive and abstractive summarization?
  2. - 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?
  2. - 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?
  2. - A) Word2Vec
  • B) BERT
  • C) BART
  • D) LSTM
  1. What is a key advantage of abstractive summarization over extractive summarization?
  2. - 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?
  2. - A) NLTK
  • B) Gensim
  • C) TensorFlow
  • D) Hugging Face Transformers