CS224N: Query Focused Multi-Document Summarization

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CS224N Query Focused Multi-Document Summarization

• Surabhi Gupta
• Mayukh Bhaowal
• Konstantin Davydov

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Problem

• A set of documents for a particular query.
• Goal Create a summary that best answers the
  query.
• First step Find relevant sentences to the query
  from the input set of documents.
• Second step Construct a summary using these
  sentences.

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Sentence Weighting

• We go through all the sentences.
• Weight of each sentence j
• Weight computed using
• Frequency
• TFIDF term frequency inverse document frequency

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Clustering

• 25-50 documents gt redundancy
• Cluster the sentences based on similarity
• Unigram
• Sentence alignment
• Put best sentence from each cluster in the
  summary

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Results using ROUGE-1

• TFIDF with C2 performs best (38.8 best DUC
  system had a score of 45.85)
• C1 clustering using unigram
• C2 clustering using sentence alignment

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Query Expansion

• Try to expand the query by adding more words
  which are relevant to the original query.
• Train a logistic regression model with features
• Wordnet similarity
• Part of speech
• Location within document
• Results were not satisfactory, but we plan to use
  better features such as co-occurrence with query
  terms, distributional similarity.