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Found 10 papers, 2 papers with code
Effective and Interpretable Information Aggregation with Capacity Networks
How to aggregate information from multiple instances is a key question multiple instance learning.
1-WL Expressiveness Is (Almost) All You Need
It has been shown that a message passing neural networks (MPNNs), a popular family of neural networks for graph-structured data, are at most as expressive as the first-order Weisfeiler-Leman (1-WL) graph isomorphism test, which has motivated the development of more expressive architectures.
Learning Analogy-Preserving Sentence Embeddings for Answer Selection
Answer selection aims at identifying the correct answer for a given question from a set of potentially correct answers.
Neural Non-additive Utility Aggregation
Neural architectures for set regression problems aim at learning representations such that good predictions can be made based on the learned representations.
Estimating Summary Quality with Pairwise Preferences
In our experiments, we show that humans are able to provide useful feedback in the form of pairwise preferences.
Which Scores to Predict in Sentence Regression for Text Summarization?
The task of automatic text summarization is to generate a short text that summarizes the most important information in a given set of documents.
The Next Step for Multi-Document Summarization: A Heterogeneous Multi-Genre Corpus Built with a Novel Construction Approach
In a detailed analysis, we show that our new corpus is significantly different from the homogeneous corpora commonly used, and that it is heterogeneous along several dimensions.
Sequential Clustering and Contextual Importance Measures for Incremental Update Summarization
In this paper, we propose a combination of sequential clustering and contextual importance measures to identify important sentences in a stream of documents in a timely manner.
