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Found 12 papers, 2 papers with code
Extractive Summarization under Strict Length Constraints
In this paper we report a comparison of various techniques for single-document extractive summarization under strict length budgets, which is a common commercial use case (e. g. summarization of news articles by news aggregators).
Unsupervised Neologism Normalization Using Embedding Space Mapping
This paper presents an approach for detecting and normalizing neologisms in social media content.
Learning to Create Better Ads: Generation and Ranking Approaches for Ad Creative Refinement
Since major ad platforms typically run A/B tests for multiple advertisers in parallel, we explore the possibility of collaboratively learning ad creative refinement via A/B tests of multiple advertisers.
Effective Few-Shot Classification with Transfer Learning
In this work, we use the ARSC dataset to study a simple application of transfer learning approaches to few-shot classification.
Powering COVID-19 community Q&A with Curated Side Information
In this work, we demonstrate the effectiveness of different attention based neural models that can directly exploit side information available in technical documents or verified forums (e. g., research publications on COVID-19 or WHO website).
Salient Object-Aware Background Generation using Text-Guided Diffusion Models
Generating background scenes for salient objects plays a crucial role across various domains including creative design and e-commerce, as it enhances the presentation and context of subjects by integrating them into tailored environments.
