As large language models improve, there is increasing interest in techni...
Recent work has shown that fine-tuning large pre-trained language models...
Scaling up language models has led to unprecedented performance gains, b...
Prompting large language models has enabled significant recent progress ...
Large language models (LLMs) have exhibited remarkable capabilities in
l...
Self-supervised pretraining has made few-shot learning possible for many...
Mixture of Experts layers (MoEs) enable efficient scaling of language mo...
Large-scale autoregressive language models such as GPT-3 are few-shot
le...
Text clustering methods were traditionally incorporated into multi-docum...
Keyphrase extraction has been comprehensively researched within the
sing...
We introduce iFacetSum, a web application for exploring topical document...
Advances in deep learning have led to promising progress in inferring
gr...
The progress in Query-focused Multi-Document Summarization (QMDS) has be...
Policy gradients-based reinforcement learning has proven to be a promisi...
Allowing users to interact with multi-document summarizers is a promisin...
Multi-document summarization (MDS) is a challenging task, often decompos...
Domain adaptation performance of a learning algorithm on a target domain...
Architecture search is the process of automatically learning the neural ...
Conducting a manual evaluation is considered an essential part of summar...
Multi-task learning (MTL) has achieved success over a wide range of prob...
Current dialogue systems focus more on textual and speech context knowle...
Sentence simplification aims to improve readability and understandabilit...
An accurate abstractive summary of a document should contain all its sal...
Abstractive text summarization is the task of compressing and rewriting ...
Sequence-to-sequence models have shown promising improvements on the tem...
Video captioning, the task of describing the content of a video, has see...