Collective intelligence underpins the success of groups, organizations, markets and
societies. Through distributed cognition and coordination, collectives can achieve outcomes …

Best-worst scaling (BWS) is a theory-driven choice experiment used for the prioritization of a
finite number of options. Within the context of prioritization, BWS is also known as MaxDiff …

Recently, there has been growing interest in extending the context length of large language
models (LLMs), aiming to effectively process long inputs of one turn or conversations with …

Abstract Large Language Models (LLMs) have made remarkable progress in processing
extensive contexts, with the Key-Value (KV) cache playing a vital role in enhancing their …

We propose a method for unsupervised abstractive opinion summarization, that combines
the attributability and scalability of extractive approaches with the coherence and fluency of …

Generic summaries try to cover an entire document and query-based summaries try to
answer document-specific questions. But real users' needs often fall in between these …

Improvements in language models' capabilities have pushed their applications towards
longer contexts, making long-context evaluation and development an active research area …

In the field of text summarization, extractive techniques aim to extract key sentences from a
document to form a summary. However, traditional methods are not sensitive enough to …

Recent advances in text autoencoders have significantly improved the quality of the latent
space, which enables models to generate grammatical and consistent text from aggregated …

Opinion summarization is the task of automatically generating summaries that encapsulate
information from multiple user reviews. We present Semantic Autoencoder (SemAE) to …