Despite the remarkable ability of large language models (LMs) to comprehend and generate
language, they have a tendency to hallucinate and create factually inaccurate output …

Answering non-factoid questions (NFQA) is a challenging task, requiring passage-level
answers that are difficult to construct and evaluate. Search engines may provide a summary …

It has been shown that the interpretability of search results is enhanced when query aspects
covered by documents are explicitly provided. However, existing work on aspect-oriented …

With the development of pre-trained language models and large-scale datasets, automatic
text summarization has attracted much attention from the community of natural language …

As free online encyclopedias with massive volumes of content, Wikipedia and Wikidata are
key to many Natural Language Processing (NLP) tasks, such as information retrieval …

Automatic summarization is attracting increasing attention as one of the most promising
research areas. This technology has been tried in various real-world applications in recent …

In this work, we try to decipher the internal connection of NLP technology development in the
past decades, searching for essence, which rewards us with a (potential) new learning …

We investigate how different domains are encoded in modern neural network architectures.
We analyze the relationship between natural language domains, model size, and the …

We describe our multi-task learning based ap-proach for summarization of real-life
dialogues as part of the DialogSum Challenge shared task at INLG 2022. Our approach …

Automatic summarization has consistently attracted attention, due to its versatility and wide
application in various downstream tasks. Despite its popularity, we find that annotation …