Search Results for author:
Found 5 papers, 2 papers with code
CO-Search: COVID-19 Information Retrieval with Semantic Search, Question Answering, and Abstractive Summarization
The COVID-19 global pandemic has resulted in international efforts to understand, track, and mitigate the disease, yielding a significant corpus of COVID-19 and SARS-CoV-2-related publications across scientific disciplines.
Improving Abstraction in Text Summarization
Abstractive text summarization aims to shorten long text documents into a human readable form that contains the most important facts from the original document.
Ranked #4 on
Text Summarization
on CNN / Daily Mail (Anonymized)
A Deep Reinforced Model for Abstractive Summarization
We introduce a neural network model with a novel intra-attention that attends over the input and continuously generated output separately, and a new training method that combines standard supervised word prediction and reinforcement learning (RL).
Ranked #6 on
Text Summarization
on CNN / Daily Mail (Anonymized)
Ask Me Anything: Dynamic Memory Networks for Natural Language Processing
Most tasks in natural language processing can be cast into question answering (QA) problems over language input.
Ranked #66 on
Sentiment Analysis
on SST-2 Binary classification
Global Belief Recursive Neural Networks
Recursive Neural Networks have recently obtained state of the art performance on several natural language processing tasks.
