Abstract Automatic Text Summarization (ATS) is an important area in Natural Language
Processing (NLP) with the goal of shortening a long text into a more compact version by …

A character network is a graph extracted from a narrative in which vertices represent
characters and edges correspond to interactions between them. A number of narrative …

A major challenge for scaling machine learning is training models to perform tasks that are
very difficult or time-consuming for humans to evaluate. We present progress on this …

Reading comprehension (RC)—in contrast to information retrieval—requires integrating
information and reasoning about events, entities, and their relations across a full document …

The quadratic computational and memory complexities of large Transformers have limited
their scalability for long document summarization. In this paper, we propose Hepos, a novel …

We introduce SummScreen, a summarization dataset comprised of pairs of TV series
transcripts and human written recaps. The dataset provides a challenging testbed for …

The task of automatic document summarization aims at generating short summaries for
originally long documents. A good summary should cover the most important information of …

High quality data forms the bedrock for building meaningful statistical models in NLP.
Consequently, data quality must be evaluated either during dataset construction or* post …

We present an approach for extractive single-document summarization. Our approach is
based on a weighted graphical representation of documents obtained by topic modeling. We …

Aspect-based summarization is the task of generating focused summaries based on specific
points of interest. Such summaries aid efficient analysis of text, such as quickly …