1. CS224N: Query Focused Multi-Document Summarization
Surabhi Gupta
Mayukh Bhaowal
Konstantin Davydov

2. 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.

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

4. Clustering 25-50 documents => redundancy
Cluster the sentences based on similarity
Unigram
Sentence alignment
Put “best” sentence from each cluster in the summary

5. 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

6. 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.