136 research outputs found
Improving the Factual Accuracy of Abstractive Clinical Text Summarization using Multi-Objective Optimization
While there has been recent progress in abstractive summarization as applied
to different domains including news articles, scientific articles, and blog
posts, the application of these techniques to clinical text summarization has
been limited. This is primarily due to the lack of large-scale training data
and the messy/unstructured nature of clinical notes as opposed to other domains
where massive training data come in structured or semi-structured form.
Further, one of the least explored and critical components of clinical text
summarization is factual accuracy of clinical summaries. This is specifically
crucial in the healthcare domain, cardiology in particular, where an accurate
summary generation that preserves the facts in the source notes is critical to
the well-being of a patient. In this study, we propose a framework for
improving the factual accuracy of abstractive summarization of clinical text
using knowledge-guided multi-objective optimization. We propose to jointly
optimize three cost functions in our proposed architecture during training:
generative loss, entity loss and knowledge loss and evaluate the proposed
architecture on 1) clinical notes of patients with heart failure (HF), which we
collect for this study; and 2) two benchmark datasets, Indiana University Chest
X-ray collection (IU X-Ray), and MIMIC-CXR, that are publicly available. We
experiment with three transformer encoder-decoder architectures and demonstrate
that optimizing different loss functions leads to improved performance in terms
of entity-level factual accuracy.Comment: Accepted to EMBC 202
Text Summarization Techniques: A Brief Survey
In recent years, there has been a explosion in the amount of text data from a
variety of sources. This volume of text is an invaluable source of information
and knowledge which needs to be effectively summarized to be useful. In this
review, the main approaches to automatic text summarization are described. We
review the different processes for summarization and describe the effectiveness
and shortcomings of the different methods.Comment: Some of references format have update
Semantics-driven Abstractive Document Summarization
The evolution of the Web over the last three decades has led to a deluge of scientific and news articles on the Internet. Harnessing these publications in different fields of study is critical to effective end user information consumption. Similarly, in the domain of healthcare, one of the key challenges with the adoption of Electronic Health Records (EHRs) for clinical practice has been the tremendous amount of clinical notes generated that can be summarized without which clinical decision making and communication will be inefficient and costly. In spite of the rapid advances in information retrieval and deep learning techniques towards abstractive document summarization, the results of these efforts continue to resemble extractive summaries, achieving promising results predominantly on lexical metrics but performing poorly on semantic metrics. Thus, abstractive summarization that is driven by intrinsic and extrinsic semantics of documents is not adequately explored. Resources that can be used for generating semantics-driven abstractive summaries include: • Abstracts of multiple scientific articles published in a given technical field of study to generate an abstractive summary for topically-related abstracts within the field, thus reducing the load of having to read semantically duplicate abstracts on a given topic. • Citation contexts from different authoritative papers citing a reference paper can be used to generate utility-oriented abstractive summary for a scientific article. • Biomedical articles and the named entities characterizing the biomedical articles along with background knowledge bases to generate entity and fact-aware abstractive summaries. • Clinical notes of patients and clinical knowledge bases for abstractive clinical text summarization using knowledge-driven multi-objective optimization. In this dissertation, we develop semantics-driven abstractive models based on intra- document and inter-document semantic analyses along with facts of named entities retrieved from domain-specific knowledge bases to produce summaries. Concretely, we propose a sequence of frameworks leveraging semantics at various granularity (e.g., word, sentence, document, topic, citations, and named entities) levels, by utilizing external resources. The proposed frameworks have been applied to a range of tasks including 1. Abstractive summarization of topic-centric multi-document scientific articles and news articles. 2. Abstractive summarization of scientific articles using crowd-sourced citation contexts. 3. Abstractive summarization of biomedical articles clustered based on entity-relatedness. 4. Abstractive summarization of clinical notes of patients with heart failure and Chest X-Rays recordings. The proposed approaches achieve impressive performance in terms of preserving semantics in abstractive summarization while paraphrasing. For summarization of topic-centric multiple scientific/news articles, we propose a three-stage approach where abstracts of scientific articles or news articles are clustered based on their topical similarity determined from topics generated using Latent Dirichlet Allocation (LDA), followed by extractive phase and abstractive phase. Then, in the next stage, we focus on abstractive summarization of biomedical literature where we leverage named entities in biomedical articles to 1) cluster related articles; and 2) leverage the named entities towards guiding abstractive summarization. Finally, in the last stage, we turn to external resources such as citation contexts pointing to a scientific article to generate a comprehensive and utility-centric abstractive summary of a scientific article, domain-specific knowledge bases to fill gaps in information about entities in a biomedical article to summarize and clinical notes to guide abstractive summarization of clinical text. Thus, the bottom-up progression of exploring semantics towards abstractive summarization in this dissertation starts with (i) Semantic Analysis of Latent Topics; builds on (ii) Internal and External Knowledge-I (gleaned from abstracts and Citation Contexts); and extends it to make it comprehensive using (iii) Internal and External Knowledge-II (Named Entities and Knowledge Bases)
Discharge Summary Hospital Course Summarisation of In Patient Electronic Health Record Text with Clinical Concept Guided Deep Pre-Trained Transformer Models
Brief Hospital Course (BHC) summaries are succinct summaries of an entire
hospital encounter, embedded within discharge summaries, written by senior
clinicians responsible for the overall care of a patient. Methods to
automatically produce summaries from inpatient documentation would be
invaluable in reducing clinician manual burden of summarising documents under
high time-pressure to admit and discharge patients. Automatically producing
these summaries from the inpatient course, is a complex, multi-document
summarisation task, as source notes are written from various perspectives (e.g.
nursing, doctor, radiology), during the course of the hospitalisation. We
demonstrate a range of methods for BHC summarisation demonstrating the
performance of deep learning summarisation models across extractive and
abstractive summarisation scenarios. We also test a novel ensemble extractive
and abstractive summarisation model that incorporates a medical concept
ontology (SNOMED) as a clinical guidance signal and shows superior performance
in 2 real-world clinical data sets
Neural Summarization of Electronic Health Records
Hospital discharge documentation is among the most essential, yet
time-consuming documents written by medical practitioners. The objective of
this study was to automatically generate hospital discharge summaries using
neural network summarization models. We studied various data preparation and
neural network training techniques that generate discharge summaries. Using
nursing notes and discharge summaries from the MIMIC-III dataset, we studied
the viability of the automatic generation of various sections of a discharge
summary using four state-of-the-art neural network summarization models (BART,
T5, Longformer and FLAN-T5). Our experiments indicated that training
environments including nursing notes as the source, and discrete sections of
the discharge summary as the target output (e.g. "History of Present Illness")
improve language model efficiency and text quality. According to our findings,
the fine-tuned BART model improved its ROUGE F1 score by 43.6% against its
standard off-the-shelf version. We also found that fine-tuning the baseline
BART model with other setups caused different degrees of improvement (up to 80%
relative improvement). We also observed that a fine-tuned T5 generally achieves
higher ROUGE F1 scores than other fine-tuned models and a fine-tuned FLAN-T5
achieves the highest ROUGE score overall, i.e., 45.6. For majority of the
fine-tuned language models, summarizing discharge summary report sections
separately outperformed the summarization the entire report quantitatively. On
the other hand, fine-tuning language models that were previously instruction
fine-tuned showed better performance in summarizing entire reports. This study
concludes that a focused dataset designed for the automatic generation of
discharge summaries by a language model can produce coherent Discharge Summary
sections
Survey on Multi-Document Summarization: Systematic Literature Review
In this era of information technology, abundant information is available on
the internet in the form of web pages and documents on any given topic. Finding
the most relevant and informative content out of these huge number of
documents, without spending several hours of reading has become a very
challenging task. Various methods of multi-document summarization have been
developed to overcome this problem. The multi-document summarization methods
try to produce high-quality summaries of documents with low redundancy. This
study conducts a systematic literature review of existing methods for
multi-document summarization methods and provides an in-depth analysis of
performance achieved by these methods. The findings of the study show that more
effective methods are still required for getting higher accuracy of these
methods. The study also identifies some open challenges that can gain the
attention of future researchers of this domain
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