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Found 6 papers, 2 papers with code
RankSHAP: a Gold Standard Feature Attribution Method for the Ranking Task
Next, we introduce Rank-SHAP, a feature attribution algorithm for the general ranking task, which is an extension to classical Shapley values.
Uncertainty in Additive Feature Attribution methods
As a result, we propose a measure to quantify the relative complexity of a blackbox classifier.
Rank-LIME: Local Model-Agnostic Feature Attribution for Learning to Rank
In this work, we extend LIME to propose Rank-LIME, a model-agnostic, local, post-hoc linear feature attribution method for the task of learning to rank that generates explanations for ranked lists.
Corpora Evaluation and System Bias Detection in Multi-document Summarization
Owing to no standard definition of the task, we encounter a plethora of datasets with varying levels of overlap and conflict between participating documents.
Neural Abstractive Summarization with Structural Attention
This problem is exacerbated in multi-document summarization tasks such as summarizing the popular opinion in threads present in community question answering (CQA) websites such as Yahoo!
Abstractive Text Summarization
Community Question Answering
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