QuOTeS: Query-Oriented Technical Summarization

Abstract. When writing an academic paper, researchers often spend considerable time reviewing and summarizing papers to extract relevant citations and data to compose the Introduction and Related Work sections. To address this problem, we propose QuOTeS, an interactive system designed to retrieve sentences related to a summary of the research from a collection of potential references and hence assist in the composition of new papers. QuOTeS integrates techniques from Query-Focused Extractive Summarization and High-Recall Information Retrieval to provide Interactive Query-Focused Summarization of scientific documents. To measure the performance of our system, we carried out a comprehensive user study where participants uploaded papers related to their research and evaluated the system in terms of its usability and the quality of the summaries it produces. The results show that QuOTeS provides a positive user experience and consistently provides query-focused summaries that are relevant, concise, and complete. We share the code of our system and the novel Query-Focused Summarization dataset collected during our experiments at https://github.com/jarobyte91/quotes.

Post-OCR Document Correction with large Ensembles of Character Sequence Models

In this paper, we propose a novel method based on character sequence-to-sequence models to correct documents already processed with Optical Character Recognition (OCR) systems. The main contribution of this paper is a set of strategies to accurately process strings much longer than the ones used to train the sequence model while being sample- and resource-efficient, supported by thorough experimentation. The strategy with the best performance involves splitting the input document in character n-grams and combining their individual corrections into the final output using a voting scheme that is equivalent to an ensemble of a large number of sequence models. We further investigate how to weigh the contributions from each one of the members of this ensemble. We test our method on nine languages of the ICDAR 2019 competition on post-OCR text correction and achieve a new state-of-the-art performance in five of them. Our code for post-OCR correction is shared at https://github.com/jarobyte91/post_ocr_correction.

Using Molecular Embeddings in QSAR Modeling: Does it Make a Difference?

Several novel algorithms for learning molecular representations have been proposed recently with the consolidation of deep learning in computer-aided drug design. Learned molecular embeddings allow attaining rich representations of the molecular structure and physical-chemical properties while overcoming several limitations of traditional molecular representations. Despite their theoretical benefits, it is not clear how molecular embeddings compare with each other and with traditional representations, which in turn hinders the process of choosing a suitable embedding algorithm for QSAR modeling. A reason for this lack of consensus is that a fair and thorough comparison of different approaches is not straightforward. To close this gap, we reproduced three unsupervised and two supervised molecular embedding techniques recently proposed in the literature. Through a thorough experimental setup, we compared the molecular representations of these five methods concerning their performance in QSAR scenarios using five different datasets with varying class imbalance levels. We also compared these representations to traditional molecular representations, namely molecular descriptors and fingerprints. Our results show that molecular embeddings did not significantly surpass baseline results obtained using traditional molecular representations. While supervised techniques yielded competitive results compared to those obtained by traditional molecular representations, unsupervised techniques did not match the baseline results. Our results motivate a discussion about the usefulness of molecular embeddings in QSAR modeling and their potential in other drug design areas, such as similarity analysis and de novo drug design.