In this article, we will learn about the fundamentals of Text Summarization, some of the different ways in which we can summarize text, Transformers, the BART model, and finally, we will practically implement some of these concepts....

Named Entity Recognition NER is field of computer science and natural language processing that deals with the identification and classification of named entities in text. The aim is to automatically extract information from unstructured text such a...

Automatic text summarization systems commonly involve humans for preparing data or evaluating model performance, yet, there lacks a systematic understanding of humans' roles, experience, and needs when interacting with or being assisted by AI. From a human-centered perspective, we map the design opportunities and considerations for human-AI interaction in text summarization and broader text generation tasks. We first conducted a systematic literature review of 70 papers, developing a taxonomy of...

Abstract Despite their widespread success, end to end transformer models consistently fall short in settings involving complex reasoning. Transformers trained on question answering QA tasks that seemingly require multiple steps of reasoning often a...

Use summarization, single linear model, and bootstrapping to understand what economic activities involve larger pay gap for women....