Self-Repetition in Abstractive Neural Summarizers.

We provide a quantitative and qualitative analysis of self-repetition in the output of neural summarizers. We measure self-repetition as the number of -grams of length four or longer that appear in multiple outputs of the same system. We analyze the behavior of three popular architectures (BART, T5 and Pegasus), fine-tuned on five datasets.

Understanding Clinical Trial Reports: Extracting Medical Entities and Their Relations.

The best evidence concerning comparative treatment effectiveness comes from clinical trials, the results of which are reported in unstructured articles. Medical experts must manually extract information from articles to inform decision-making, which is time-consuming and expensive. Here we consider the task of both (a) extracting treatments and outcomes from full-text articles describing clinical trials (entity identification) and, (b) inferring the reported results for the former with respect to the latter (relation extraction).

: Mapping and Browsing Medical Evidence in Real-Time.

We introduce , a living database of clinical trial reports. Here we mainly describe the component; this extracts from biomedical abstracts key pieces of information that clinicians need when appraising the literature, and also the relations between these. Specifically, the system extracts descriptions of trial participants, the treatments compared in each arm (the ), and which outcomes were measured.

State of the evidence: a survey of global disparities in clinical trials.

Introduction: Ideally, health conditions causing the greatest global disease burden should attract increased research attention. We conducted a comprehensive global study investigating the number of randomised controlled trials (RCTs) published on different health conditions, and how this compares with the global disease burden that they impose.

Methods: We use machine learning to monitor PubMed daily, and find and analyse RCT reports.

Trialstreamer: A living, automatically updated database of clinical trial reports.

Objective: Randomized controlled trials (RCTs) are the gold standard method for evaluating whether a treatment works in health care but can be difficult to find and make use of. We describe the development and evaluation of a system to automatically find and categorize all new RCT reports.

Materials And Methods: Trialstreamer continuously monitors PubMed and the World Health Organization International Clinical Trials Registry Platform, looking for new RCTs in humans using a validated classifier.

Syntactic Patterns Improve Information Extraction for Medical Search.

Medical professionals search the published literature by specifying the type of , the medical and the measure(s) of interest. In this paper we demonstrate how features encoding syntactic patterns improve the performance of state-of-the-art sequence tagging models (both linear and neural) for information extraction of these medically relevant categories. We present an analysis of the type of patterns exploited, and the semantic space induced for these, i.

A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature.

We present a corpus of 5,000 richly annotated abstracts of medical articles describing clinical randomized controlled trials. Annotations include demarcations of text spans that describe the Patient population enrolled, the Interventions studied and to what they were Compared, and the Outcomes measured (the 'PICO' elements). These spans are further annotated at a more granular level, e.

Aggregating and Predicting Sequence Labels from Crowd Annotations.

Despite sequences being core to NLP, scant work has considered how to handle noisy sequence labels from multiple annotators for the same text. Given such annotations, we consider two complementary tasks: (1) aggregating sequential crowd labels to infer a best single set of consensus annotations; and (2) using crowd annotations as training data for a model that can predict sequences in unannotated text. For aggregation, we propose a novel Hidden Markov Model variant.

CREMA-D: Crowd-sourced Emotional Multimodal Actors Dataset.

People convey their emotional state in their face and voice. We present an audio-visual data set uniquely suited for the study of multi-modal emotion expression and perception. The data set consists of facial and vocal emotional expressions in sentences spoken in a range of basic emotional states (happy, sad, anger, fear, disgust, and neutral).

Lexical use in emotional autobiographical narratives of persons with schizophrenia and healthy controls.

Language dysfunction has long been described in schizophrenia and most studies have focused on characteristics of structure and form. This project focuses on the content of language based on autobiographical narratives of five basic emotions. In persons with schizophrenia and healthy controls, we employed a comprehensive automated analysis of lexical use and we identified specific words and semantically or functionally related words derived from dictionaries that occurred significantly more often in narratives of either group.

Speaker-sensitive emotion recognition via ranking: Studies on acted and spontaneous speech.

We introduce a ranking approach for emotion recognition which naturally incorporates information about the general expressivity of speakers. We demonstrate that our approach leads to substantial gains in accuracy compared to conventional approaches. We train ranking SVMs for individual emotions, treating the data from each speaker as a separate query, and combine the predictions from all rankers to perform multi-class prediction.

Acoustic and Lexical Representations for Affect Prediction in Spontaneous Conversations.

In this article we investigate what representations of acoustics and word usage are most suitable for predicting dimensions of affect|AROUSAL, VALANCE, POWER and EXPECTANCY|in spontaneous interactions. Our experiments are based on the AVEC 2012 challenge dataset. For lexical representations, we compare corpus-independent features based on psychological word norms of emotional dimensions, as well as corpus-dependent representations.

Temporal Bayesian Fusion for Affect Sensing: Combining Video, Audio, and Lexical Modalities.

The affective state of people changes in the course of conversations and these changes are expressed externally in a variety of channels, including facial expressions, voice, and spoken words. Recent advances in automatic sensing of affect, through cues in individual modalities, have been remarkable; yet emotion recognition is far from a solved problem. Recently, researchers have turned their attention to the problem of multimodal affect sensing in the hope that combining different information sources would provide great improvements.

Prosodic cues for emotion: analysis with discrete characterization of intonation.

In this paper we study the relationship between acted perceptually unambiguous emotion and prosody. Unlike most contemporary approaches which base the analysis of emotion in voice solely on continuous features extracted automatically from the acoustic signal, we analyze the predictive power of discrete characterizations of intonations in the ToBI framework. The goal of our work is to test if particular discrete prosodic events provide significant discriminative power for emotion recognition.

Action Unit Models of Facial Expression of Emotion in the Presence of Speech.

Automatic recognition of emotion using facial expressions in the presence of speech poses a unique challenge because talking reveals clues for the affective state of the speaker but distorts the canonical expression of emotion on the face. We introduce a corpus of acted emotion expression where speech is either present (talking) or absent (silent). The corpus is uniquely suited for analysis of the interplay between the two conditions.

Combining Video, Audio and Lexical Indicators of Affect in Spontaneous Conversation via Particle Filtering.

We present experiments on fusing facial video, audio and lexical indicators for affect estimation during dyadic conversations. We use temporal statistics of texture descriptors extracted from facial video, a combination of various acoustic features, and lexical features to create regression based affect estimators for each modality. The single modality regressors are then combined using particle filtering, by treating these independent regression outputs as measurements of the affect states in a Bayesian filtering framework, where previous observations provide prediction about the current state by means of learned affect dynamics.

Class-Level Spectral Features for Emotion Recognition.

The most common approaches to automatic emotion recognition rely on utterance level prosodic features. Recent studies have shown that utterance level statistics of segmental spectral features also contain rich information about expressivity and emotion. In our work we introduce a more fine-grained yet robust set of spectral features: statistics of Mel-Frequency Cepstral Coefficients computed over three phoneme type classes of interest-stressed vowels, unstressed vowels and consonants in the utterance.

Beyond SumBasic: Task-focused summarization with sentence simplification and lexical expansion.

In recent years, there has been increased interest in topic-focused multi-document summarization. In this task, automatic summaries are produced in response to a specific information request, or topic, stated by the user. The system we have designed to accomplish this task comprises four main components: a generic extractive summarization system, a topic-focusing component, sentence simplification, and lexical expansion of topic words.