Search Results for author:
Found 17 papers, 6 papers with code
Rewards with Negative Examples for Reinforced Topic-Focused Abstractive Summarization
We introduce a deep reinforcement learning approach to topic-focused abstractive summarization, trained on rewards with a novel negative example baseline.
NewsQs: Multi-Source Question Generation for the Inquiring Mind
We present NewsQs (news-cues), a dataset that provides question-answer pairs for multiple news documents.
Background Summarization of Event Timelines
Generating concise summaries of news events is a challenging natural language processing task.
Generating Summaries with Controllable Readability Levels
Readability refers to how easily a reader can understand a written text.
On Conditional and Compositional Language Model Differentiable Prompting
Prompts have been shown to be an effective method to adapt a frozen Pretrained Language Model (PLM) to perform well on downstream tasks.
Faithfulness-Aware Decoding Strategies for Abstractive Summarization
We present a systematic study of the effect of generation techniques such as beam search and nucleus sampling on faithfulness in abstractive summarization.
Efficient Few-Shot Fine-Tuning for Opinion Summarization
In the same vein, we pre-train the adapters in a query-based manner on customer reviews and then fine-tune them on annotated datasets.
FactGraph: Evaluating Factuality in Summarization with Semantic Graph Representations
Despite recent improvements in abstractive summarization, most current approaches generate summaries that are not factually consistent with the source document, severely restricting their trust and usage in real-world applications.
Evaluating the Tradeoff Between Abstractiveness and Factuality in Abstractive Summarization
Neural models for abstractive summarization tend to generate output that is fluent and well-formed but lacks semantic faithfulness, or factuality, with respect to the input documents.
Efficiently Summarizing Text and Graph Encodings of Multi-Document Clusters
We also show improvements in a transfer-only setup on the DUC-2004 dataset.
Transductive Learning for Abstractive News Summarization
This applies to scenarios such as a news publisher training a summarizer on dated news and summarizing incoming recent news.
Multi-Task Networks With Universe, Group, and Task Feature Learning
We present methods for multi-task learning that take advantage of natural groupings of related tasks.
Just ASK: Building an Architecture for Extensible Self-Service Spoken Language Understanding
This paper presents the design of the machine learning architecture that underlies the Alexa Skills Kit (ASK) a large scale Spoken Language Understanding (SLU) Software Development Kit (SDK) that enables developers to extend the capabilities of Amazon's virtual assistant, Alexa.
Transfer Learning for Neural Semantic Parsing
The goal of semantic parsing is to map natural language to a machine interpretable meaning representation language (MRL).
