Recent Deep Learning (DL) summarization models greatly outperform traditional summarization methodologies, generating high-quality summaries. Despite their success, there are still important open issues, such as the limited engagement and trust of users in the whole process. In order to overcome these issues, we reconsider the task of summarization from a human-centered perspective. We propose to integrate a user interface with an underlying DL model, instead of tackling summarization as an isolated task from the end user. We present a novel system, where the user can actively participate in the whole summarization process. We also enable the user to gather insights into the causative factors that drive the model’s behavior, exploiting the self-attention mechanism. We focus on the financial domain, in order to demonstrate the efficiency of generic DL models for domain-specific applications. Our work takes a first step towards a model-interface co-design approach, where DL models evolve along user needs, paving the way towards human-computer text summarization interfaces.

This work revisits the information given by the graph-of-words and its typical utilization through graph-based ranking approaches in the context of keyword extraction. Recent, well-known graph-based approaches typically employ the knowledge from word vector representations during the ranking process via popular centrality measures (e.g., PageRank) without giving the primary role to vectors’ distribution. We consider the adjacency matrix that corresponds to the graph-of-words of a target text document as the vector representation of its vocabulary. We propose the distribution-based modeling of this adjacency matrix using unsupervised (learning) algorithms. The efficacy of the distribution-based modeling approaches compared to state-of-the-art graph-based methods is confirmed by an extensive experimental study according to the F1 score. Our code is available on GitHub.

Automatically extracting keyphrases from scholarly documents leads to a valuable concise representation that humans can understand and machines can process for tasks, such as information retrieval, article clustering and article classification. This paper is concerned with the parts of a scientific article that should be given as input to keyphrase extraction methods. Recent deep learning methods take titles and abstracts as input due to the increased computational complexity in processing long sequences, whereas traditional approaches can also work with full-texts. Titles and abstracts are dense in keyphrases, but often miss important aspects of the articles, while full-texts on the other hand are richer in keyphrases but much noisier. To address this trade-off, we propose the use of extractive summarization models on the full-texts of scholarly documents. Our empirical study on 3 article collections using 3 keyphrase extraction methods shows promising results.

We present the systems we submitted for the shared tasks of the Workshop on Scholarly Document Processing at EMNLP 2020. Our approaches to the tasks are focused on exploiting large Transformer models pre-trained on huge corpora and adapting them to the different shared tasks. For tasks 1A and 1B of CL-SciSumm we are using different variants of the BERT model to tackle the tasks of “cited text span” and “facet” identification. For the summarization tasks 2 of CL-SciSumm, LaySumm and LongSumm we make use of different variants of the PEGASUS model, with and without fine-tuning, adapted to the nuances of each one of those particular tasks.