Paper Review - On Faithfulness and Factuality in Abstractive Summarization
Authors: Joshua Maynez, Shashi Narayan, Bernd Bohnet, Ryan McDonald
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Paper Review - Learning Neural Templates for Text Generation
Authors: Sam Wiseman, Stuart M. Shieber, Alexander M. Rush
Paper Review - Bottom-Up Abstractive Summarization
Authors: Sebastian Gehrmann, Yuntian Deng, Alexander M. Rush
Paper Review - Improving Zero and Few-Shot Abstractive Summarization with Intermediate Fine-tuning and Data Augmentation
Authors: Alexander R. Fabbri, Simeng Han, Haoyuan Li, Haoran Li, Marjan Ghazvininejad, Shafiq Joty, Dragomir Radev, Yashar Mehdad
Paper Review - Decoupling Representation and Classifier for Long-Tailed Recognition
Authors: Bingyi Kang, Saining Xie, Marcus Rohrbach, Zhicheng Yan, Albert Gordo, Jiashi Feng, Yannis Kalantidis
Overlook of Relation Extraction
Information Extraction v.s. Relation Extraction Information Extraction: Information extraction is the task of automatically extracting structured information from unstructured and/or semi-structured machine-readable documents and other electronically represented sources. Relation extraction (RE) is an important task in IE. It focuses on extracting relations between entities. A complete relation RE system consists of a named entity recognizer to identify named entities from text. an entity linker to link entities to existing knowledge graphs....