34 citations · 76 across the 15 of their papers we have counts for
25 papers
Abstractive Summarization Guided by Latent Hierarchical Document Structure
Yifu Qiu, Shay B. Cohen
Sequential abstractive neural summarizers often do not use the underlying structure in the input article or dependencies between the input sentences. This structure is essential to…
Understanding Domain Learning in Language Models Through Subpopulation Analysis
Zheng Zhao, Yftah Ziser, Shay B. Cohen
We investigate how different domains are encoded in modern neural network architectures. We analyze the relationship between natural language domains, model size, and the amount of…
Unsupervised Extractive Summarization by Human Memory Simulation
Ronald Cardenas, Matthias Galle, Shay B. Cohen
Summarization systems face the core challenge of identifying and selecting important information. In this paper, we tackle the problem of content selection in unsupervised extracti…
Learning to Match Mathematical Statements with Proofs
Maximin Coavoux, Shay B. Cohen
We introduce a novel task consisting in assigning a proof to a given mathematical statement. The task is designed to improve the processing of research-level mathematical texts. Ap…
Narration Generation for Cartoon Videos
Nikos Papasarantopoulos, Shay B. Cohen
Research on text generation from multimodal inputs has largely focused on static images, and less on video data. In this paper, we propose a new task, narration generation, that is…
Lightweight, Dynamic Graph Convolutional Networks for AMR-to-Text Generation
Yan Zhang, Zhijiang Guo, Zhiyang Teng +4
AMR-to-text generation is used to transduce Abstract Meaning Representation structures (AMR) into text. A key challenge in this task is to efficiently learn effective graph represe…