Purvaja  Balaji

Purvaja Balaji

Scholar Title

MIT EECS | Philips Undergraduate Research and Innovation Scholar

Research Title

Adapting PRIMERA for Query-Focused Multi-Document Summarization




Electrical Engineering and Computer Science

Research Areas
  • Machine Learning
  • Natural Language and Speech Processing

Amar Gupta


Multi-document summarization (MDS) is the task of extracting information from multiple sources, often about the same topic, into one summary. Query-focused multi-document summarization (qMDS) creates comprehensive yet concise summaries tailored to a particular query. We propose a qMDS model qPRIMERA based on the state-of-the-art MDS model PRIMERA. Our adaptations and points of experimentation are two-fold. First, we use a query-to-sentence scorer to filter unrelated sentences from the document. Second, we combine a query-to-document score with a cluster ROUGE score to identify salient sentences to mask for the Gap Sentence Generation (GSG) objective. qPRIMERA is competitive with other state-of-the-art qMDS models.

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