3 citations · 13 across the 9 of their papers we have counts for
7 papers · 1 filter
Identifying Speakers in Dialogue Transcripts: A Text-based Approach Using Pretrained Language Models
Minh Nguyen, Franck Dernoncourt, Seunghyun Yoon +6
We introduce an approach to identifying speaker names in dialogue transcripts, a crucial task for enhancing content accessibility and searchability in digital media archives. Despi…
Towards Enhancing Coherence in Extractive Summarization: Dataset and Experiments with LLMs
Mihir Parmar, Hanieh Deilamsalehy, Franck Dernoncourt +3
Extractive summarization plays a pivotal role in natural language processing due to its wide-range applications in summarizing diverse content efficiently, while also being faithfu…
LongLaMP: A Benchmark for Personalized Long-form Text Generation
Ishita Kumar, Snigdha Viswanathan, Sushrita Yerra +11
Long-text generation is seemingly ubiquitous in real-world applications of large language models such as generating an email or writing a review. Despite the fundamental importance…
Self-Debiasing Large Language Models: Zero-Shot Recognition and Reduction of Stereotypes
Isabel O. Gallegos, Ryan A. Rossi, Joe Barrow +6
Large language models (LLMs) have shown remarkable advances in language generation and understanding but are also prone to exhibiting harmful social biases. While recognition of th…
MeetingBank: A Benchmark Dataset for Meeting Summarization
Yebowen Hu, Tim Ganter, Hanieh Deilamsalehy +3
As the number of recorded meetings increases, it becomes increasingly important to utilize summarization technology to create useful summaries of these recordings. However, there i…
Curriculum-Guided Abstractive Summarization
Sajad Sotudeh, Hanieh Deilamsalehy, Franck Dernoncourt +1
Recent Transformer-based summarization models have provided a promising approach to abstractive summarization. They go beyond sentence selection and extractive strategies to deal w…
Curriculum-guided Abstractive Summarization for Mental Health Online Posts
Sajad Sotudeh, Nazli Goharian, Hanieh Deilamsalehy +1
Automatically generating short summaries from users' online mental health posts could save counselors' reading time and reduce their fatigue so that they can provide timely respons…