State-of-the-art abstractive summarization models generally rely on extensive labeled data,
which lowers their generalization ability on domains where such data are not available. In …

We present the results of three Shared Tasks held at the Scholarly Document Processing
Workshop at EMNLP2020: CL-SciSumm, LaySumm and LongSumm. We report on each of …

Phrase mining is a fundamental text mining task that aims to identify quality phrases from
context. Nevertheless, the scarcity of extensive gold labels datasets, demanding substantial …

Multimodal affect recognition constitutes an important aspect for enhancing interpersonal
relationships in human-computer interaction. However, relevant data is hard to come by and …

We study the zero-shot setting for the aspect-based scientific document summarization task.
Summarizing scientific documents with respect to an aspect can remarkably improve …

Lay summarization aims to simplify complex scientific information for non-expert audiences.
This paper investigates the trade-off between readability and relevance in the lay …

Abstractive text summarization is a widely studied problem in sequence-to-sequence
(seq2seq) architecture. BART is the state-of-the-art (SOTA) model for sequence-to-sequence …

The capability for machines to transduce, understand, and reason with natural language
lives at the heart of Artificial Intelligence not only because natural language is one of the …

Initially, we analyzed the datasets in a statistical way so as to learn about various sections'
contributions to the final summary in both the pros and life datasets. We found that both the …

Text summarization aims to generate a concise version of the original text. The longer the
summary text is, the more detailed it will be from the original text, and this depends on the …