4 research outputs found
Clickbait? Sensational Headline Generation with Auto-tuned Reinforcement Learning
Sensational headlines are headlines that capture people's attention and
generate reader interest. Conventional abstractive headline generation methods,
unlike human writers, do not optimize for maximal reader attention. In this
paper, we propose a model that generates sensational headlines without labeled
data. We first train a sensationalism scorer by classifying online headlines
with many comments ("clickbait") against a baseline of headlines generated from
a summarization model. The score from the sensationalism scorer is used as the
reward for a reinforcement learner. However, maximizing the noisy
sensationalism reward will generate unnatural phrases instead of sensational
headlines. To effectively leverage this noisy reward, we propose a novel loss
function, Auto-tuned Reinforcement Learning (ARL), to dynamically balance
reinforcement learning (RL) with maximum likelihood estimation (MLE). Human
evaluation shows that 60.8% of samples generated by our model are sensational,
which is significantly better than the Pointer-Gen baseline and other RL
models.Comment: Accepted by EMNLP201
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Generative Language Models for Personalized Information Understanding
A major challenge in information understanding stems from the diverse nature of the audience, where individuals possess varying preferences, experiences, educational and cultural backgrounds. Consequently, adopting a one-size-fits-all approach to provide information may prove suboptimal. While prior research has predominantly focused on delivering pre-existing content to users with potential interests, this thesis explores generative language models for personalized information understanding. By harnessing the potential of generative language models, our objective is to generate novel personalize content for individual users. As a result, users from diverse backgrounds can be provided with content that are tailored for their need and better aligns with their interests. The crux of this research hinges on addressing the following two aspects: 1. Personalized Content: How to harness user profiles to create tailored content for individual users; 2. Effective Communication: How to engage with users in order to proficiently convey information. For the first aspect, i.e. personalized content, we explored personalized news headline generation. By analyzing users\u27 reading history, our proposed framework identifies perspectives that users are interested in, which can further guide generating news headlines that are attractive to users. For the second aspect, i.e. effective communication, we developed personalized reading assistive agent, which assist users understand complex information in news article or academic documents through conversations. Compared to reading, obtaining information through conversations is more interactive and requires shorter attention span. We further incorporate the above aspects in personalized information systems in a real-life scenario, i.e. patient education. Specifically, we propose a novel after-visit summaries (AVS) writing assistant. After-visit summaries notes are documents given to patients to help them understand their clinical visits and disease self-management. Our approach not only automatically generates AVS drafts, but also detects potential errors in the generated drafts, allowing physicians to revise and produce AVS notes with higher efficiency and accuracy. Moreover, we present PaniniQA, a patient-centric interactive question answering system designed to help patients understand their discharge instructions. PaniniQA first identifies important clinical content from patients’ discharge instructions and then formulates personalized educational questions for distinctive patients. In addition, PaniniQA is also equipped with answer verification functionality to provide timely feedback to correct patients’ misunderstandings. Overall, we aspire to contribute to the advancement of information dissemination techniques, promoting a more inclusive and effective means of communication in our information-driven world