SciLit: A Platform for Joint Scientific Literature Discovery, Summarization and Citation Generation

Scientific writing involves retrieving, summarizing, and citing relevant papers, which can be time-consuming processes in large and rapidly evolving fields. By making these processes inter-operable, natural language processing (NLP) provides opportunities for creating end-to-end assistive writing tools. We propose SciLit, a pipeline that automatically recommends relevant papers, extracts highlights, and suggests a reference sentence as a citation of a paper, taking into consideration the user-provided context and keywords. SciLit efficiently recommends papers from large databases of hundreds of millions of papers using a two-stage pre-fetching and re-ranking literature search system that flexibly deals with addition and removal of a paper database. We provide a convenient user interface that displays the recommended papers as extractive summaries and that offers abstractively-generated citing sentences which are aligned with the provided context and which mention the chosen keyword(s). Our assistive tool for literature discovery and scientific writing is available at https://scilit.vercel.appComment: Accepted at ACL 2023 System Demonstratio

Text pre-processing tool to increase the exactness of experimental results in summarization solutions

For years, and nowadays even more because of the ease of access to information, countless scientific documents that cover all branches of human knowledge are generated. These documents, consisting mostly of text, are stored in digital libraries that are increasingly consenting access and manipulation. This has allowed these repositories of documents to be used for research work of great interest, particularly those related to evaluation of automatic summaries through experimentation. In this area of computer science, the experimental results of many of the published works are obtained using document collections, some known and others not so much, but without specifying all the special considerations to achieve said results. This produces an unfair competition in the realization of experiments when comparing results and does not allow to be objective in the obtained conclusions. This paper presents a text document manipulation tool to increase the exactness of results when obtaining, evaluating and comparing automatic summaries from different corpora. This work has been motivated by the need to have a tool that allows to process documents, split their content properly and make sure that each text snippet does not lose its contextual information. Applying the model proposed to a set of free-access scientific papers has been successful.XV Workshop Bases de Datos y Minería de Datos (WBDDM)Red de Universidades con Carreras en Informática (RedUNCI

DR.BENCH: Diagnostic Reasoning Benchmark for Clinical Natural Language Processing

The meaningful use of electronic health records (EHR) continues to progress in the digital era with clinical decision support systems augmented by artificial intelligence. A priority in improving provider experience is to overcome information overload and reduce the cognitive burden so fewer medical errors and cognitive biases are introduced during patient care. One major type of medical error is diagnostic error due to systematic or predictable errors in judgment that rely on heuristics. The potential for clinical natural language processing (cNLP) to model diagnostic reasoning in humans with forward reasoning from data to diagnosis and potentially reduce the cognitive burden and medical error has not been investigated. Existing tasks to advance the science in cNLP have largely focused on information extraction and named entity recognition through classification tasks. We introduce a novel suite of tasks coined as Diagnostic Reasoning Benchmarks, DR.BENCH, as a new benchmark for developing and evaluating cNLP models with clinical diagnostic reasoning ability. The suite includes six tasks from ten publicly available datasets addressing clinical text understanding, medical knowledge reasoning, and diagnosis generation. DR.BENCH is the first clinical suite of tasks designed to be a natural language generation framework to evaluate pre-trained language models. Experiments with state-of-the-art pre-trained generative language models using large general domain models and models that were continually trained on a medical corpus demonstrate opportunities for improvement when evaluated in DR. BENCH. We share DR. BENCH as a publicly available GitLab repository with a systematic approach to load and evaluate models for the cNLP community.Comment: Under revie

Text pre-processing tool to increase the exactness of experimental results in summarization solutions

For years, and nowadays even more because of the ease of access to information, countless scientific documents that cover all branches of human knowledge are generated. These documents, consisting mostly of text, are stored in digital libraries that are increasingly consenting access and manipulation. This has allowed these repositories of documents to be used for research work of great interest, particularly those related to evaluation of automatic summaries through experimentation. In this area of computer science, the experimental results of many of the published works are obtained using document collections, some known and others not so much, but without specifying all the special considerations to achieve said results. This produces an unfair competition in the realization of experiments when comparing results and does not allow to be objective in the obtained conclusions. This paper presents a text document manipulation tool to increase the exactness of results when obtaining, evaluating and comparing automatic summaries from different corpora. This work has been motivated by the need to have a tool that allows to process documents, split their content properly and make sure that each text snippet does not lose its contextual information. Applying the model proposed to a set of free-access scientific papers has been successful.XV Workshop Bases de Datos y Minería de Datos (WBDDM)Red de Universidades con Carreras en Informática (RedUNCI

COMPENDIUM: a text summarisation tool for generating summaries of multiple purposes, domains, and genres

In this paper, we present a Text Summarisation tool, compendium, capable of generating the most common types of summaries. Regarding the input, single- and multi-document summaries can be produced; as the output, the summaries can be extractive or abstractive-oriented; and finally, concerning their purpose, the summaries can be generic, query-focused, or sentiment-based. The proposed architecture for compendium is divided in various stages, making a distinction between core and additional stages. The former constitute the backbone of the tool and are common for the generation of any type of summary, whereas the latter are used for enhancing the capabilities of the tool. The main contributions of compendium with respect to the state-of-the-art summarisation systems are that (i) it specifically deals with the problem of redundancy, by means of textual entailment; (ii) it combines statistical and cognitive-based techniques for determining relevant content; and (iii) it proposes an abstractive-oriented approach for facing the challenge of abstractive summarisation. The evaluation performed in different domains and textual genres, comprising traditional texts, as well as texts extracted from the Web 2.0, shows that compendium is very competitive and appropriate to be used as a tool for generating summaries.This research has been supported by the project “Desarrollo de Técnicas Inteligentes e Interactivas de Minería de Textos” (PROMETEO/2009/119) and the project reference ACOMP/2011/001 from the Valencian Government, as well as by the Spanish Government (grant no. TIN2009-13391-C04-01)