We present the results of three Shared Tasks held at the Scholarly Document Processing Workshop at EMNLP2020: CL-SciSumm, LaySumm and LongSumm.
To reach to the broader NLP and AI/ML community, pool distributed efforts and enable shared access to published research, we held the 1st Workshop on Scholarly Document Processing at EMNLP 2020 as a virtual event.
A common method for extractive multi-document news summarization is to re-formulate it as a single-document summarization problem by concatenating all documents as a single meta-document.
We propose a method for online news stream clustering that is a variant of the non-parametric streaming K-means algorithm.
This overview describes the official results of the CL-SciSumm Shared Task 2018 -- the first medium-scale shared task on scientific document summarization in the computational linguistics (CL) domain.
All papers are from the open access research papers in the CL domain.
We propose novel attention based models to infer the amount of latent context necessary to predict instructor intervention.
We tackle the prediction of instructor intervention in student posts from discussion forums in Massive Open Online Courses (MOOCs).
Thus we have also applied word embeddings to the novel task of cross-lingual WSD for Chinese and provide a public dataset for further benchmarking.