Research talk: Summarizing information across multiple documents and modalities
Search engines have evolved over time, from initially providing the most relevant URLs to user queries to providing information in response to user queries that summarize content from multiple web documents. Instead of clicking and reading the top search results and finding the information themselves, users can now rely on search engines to “”read”” these documents via long document machine reading comprehension techniques and present the relevant summary from various documents to them. With recent breakthroughs in natural language generation and understanding of multi-modality content, search engines can now return very rich information presented in a comprehensive and easy-to-consume way. In this presentation, we will talk about some of the technologies behind this experience and how we see technology evolving in the future.
Learn more about the 2021 Microsoft Research Summit: https://Aka.ms/researchsummit (opens in new tab)
- Track:
- The Future of Search & Recommendation
- Date:
- Speakers:
- Subhojit Som
- Affiliation:
- Microsoft
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Subhojit Som
Principal Applied Science Manager
Microsoft
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The Future of Search & Recommendation
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Keynote: Universal search and recommendation
Speakers:- Paul Bennett
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Research talk: Learning and pretraining strategies for dense retrieval in search and beyond
Speakers:- Chenyan Xiong
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Research talk: Is phrase retrieval all we need?
Speakers:- Danqi Chen
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Research talk: Summarizing information across multiple documents and modalities
Speakers:- Subhojit Som
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Panel: The future of search and recommendation: Beyond web search
Speakers:- Eric Horvitz,
- Nitin Agrawal,
- Soumen Chakrabati
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Panel: Causality in search and recommendation systems
Speakers:- Emre Kiciman,
- Amit Sharma,
- Dean Eckles
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