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Found 8 papers, 4 papers with code
PrefScore: Pairwise Preference Learning for Reference-free Summarization Quality Assessment
Evaluating machine-generated summaries without a human-written reference summary has been a need for a long time.
DocAsRef: An Empirical Study on Repurposing Reference-Based Summary Quality Metrics Reference-Freely
Automated summary quality assessment falls into two categories: reference-based and reference-free.
Circuit Routing Using Monte Carlo Tree Search and Deep Neural Networks
Like finding paths between pairs of locations, circuit routing generates traces of wires to connect contacts or leads of circuit components.
SueNes: A Weakly Supervised Approach to Evaluating Single-Document Summarization via Negative Sampling
Canonical automatic summary evaluation metrics, such as ROUGE, focus on lexical similarity which cannot well capture semantics nor linguistic quality and require a reference summary which is costly to obtain.
Triaging moderate COVID-19 and other viral pneumonias from routine blood tests
The COVID-19 is sweeping the world with deadly consequences.
RLScheduler: An Automated HPC Batch Job Scheduler Using Reinforcement Learning
Today high-performance computing (HPC) platforms are still dominated by batch jobs.
Cross-Domain Review Helpfulness Prediction Based on Convolutional Neural Networks with Auxiliary Domain Discriminators
With the growing amount of reviews in e-commerce websites, it is critical to assess the helpfulness of reviews and recommend them accordingly to consumers.
Detecting (Un)Important Content for Single-Document News Summarization
We present a robust approach for detecting intrinsic sentence importance in news, by training on two corpora of document-summary pairs.
