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Found 6 papers, 5 papers with code
Supervising the Centroid Baseline for Extractive Multi-Document Summarization
The centroid method is a simple approach for extractive multi-document summarization and many improvements to its pipeline have been proposed.
Towards End-to-end Speech-to-text Summarization
We model S2T summarization both with a cascade and an E2E system for a corpus of broadcast news in French.
Improving abstractive summarization with energy-based re-ranking
Current abstractive summarization systems present important weaknesses which prevent their deployment in real-world applications, such as the omission of relevant information and the generation of factual inconsistencies (also known as hallucinations).
Tackling unsupervised multi-source domain adaptation with optimism and consistency
It has been known for a while that the problem of multi-source domain adaptation can be regarded as a single source domain adaptation task where the source domain corresponds to a mixture of the original source domains.
SpaMHMM: Sparse Mixture of Hidden Markov Models for Graph Connected Entities
We propose a framework to model the distribution of sequential data coming from a set of entities connected in a graph with a known topology.
Dimensional emotion recognition using visual and textual cues
This paper addresses the problem of automatic emotion recognition in the scope of the One-Minute Gradual-Emotional Behavior challenge (OMG-Emotion challenge).
