1 citations · 1 across the 6 of their papers we have counts for
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LOCOST: State-Space Models for Long Document Abstractive Summarization
Florian Le Bronnec, Song Duong, Mathieu Ravaut +6
State-space models are a low-complexity alternative to transformers for encoding long sequences and capturing long-term dependencies. We propose LOCOST: an encoder-decoder architec…
Finite-context Indexing of Restricted Output Space for NLP Models Facing Noisy Input
Minh Nguyen, Nancy F. Chen
NLP models excel on tasks with clean inputs, but are less accurate with noisy inputs. In particular, character-level noise such as human-written typos and adversarially-engineered…
Controllable Multi-document Summarization: Coverage & Coherence Intuitive Policy with Large Language Model Based Rewards
Litton J Kurisinkel, Nancy F chen
Memory-efficient large language models are good at refining text input for better readability. However, controllability is a matter of concern when it comes to text generation task…
LLM Based Multi-Document Summarization Exploiting Main-Event Biased Monotone Submodular Content Extraction
Litton J Kurisinkel, Nancy F. Chen
Multi-document summarization is a challenging task due to its inherent subjective bias, highlighted by the low inter-annotator ROUGE-1 score of 0.4 among DUC-2004 reference summari…
On Context Utilization in Summarization with Large Language Models
Mathieu Ravaut, Aixin Sun, Nancy F. Chen +1
Large language models (LLMs) excel in abstractive summarization tasks, delivering fluent and pertinent summaries. Recent advancements have extended their capabilities to handle lon…