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Found 14 papers, 2 papers with code
DynE: Dynamic Ensemble Decoding for Multi-Document Summarization
Sequence-to-sequence (s2s) models are the basis for extensive work in natural language processing.
A Large-Scale Multi-Document Summarization Dataset from the Wikipedia Current Events Portal
Multi-document summarization (MDS) aims to compress the content in large document collections into short summaries and has important applications in story clustering for newsfeeds, presentation of search results, and timeline generation.
Living a discrete life in a continuous world: Reference with distributed representations
Reference is a crucial property of language that allows us to connect linguistic expressions to the world.
Towards Multi-Agent Communication-Based Language Learning
We propose an interactive multimodal framework for language learning.
The red one!: On learning to refer to things based on their discriminative properties
As a first step towards agents learning to communicate about their visual environment, we propose a system that, given visual representations of a referent (cat) and a context (sofa), identifies their discriminative attributes, i. e., properties that distinguish them (has_tail).
Combining Language and Vision with a Multimodal Skip-gram Model
We extend the SKIP-GRAM model of Mikolov et al. (2013a) by taking visual information into account.
