4 research outputs found
Controllable Topic-Focused Abstractive Summarization
Controlled abstractive summarization focuses on producing condensed versions
of a source article to cover specific aspects by shifting the distribution of
generated text towards a desired style, e.g., a set of topics. Subsequently,
the resulting summaries may be tailored to user-defined requirements. This
paper presents a new Transformer-based architecture capable of producing
topic-focused summaries. The architecture modifies the cross-attention
mechanism of the Transformer to bring topic-focus control to the generation
process while not adding any further parameters to the model. We show that our
model sets a new state of the art on the NEWTS dataset in terms of
topic-focused abstractive summarization as well as a topic-prevalence score.
Moreover, we show via extensive experiments that our proposed topical
cross-attention mechanism can be plugged into various Transformer models, such
as BART and T5, improving their performance on the CNN/Dailymail and XSum
benchmark datasets for abstractive summarization. This is achieved via
fine-tuning, without requiring training from scratch. Finally, we show through
human evaluation that our model generates more faithful summaries outperforming
the state-of-the-art Frost model
Just-in-time information retrieval and summarization for personal assistance
With the rapid development of means for producing user-generated data opportunities for collecting such data over a time-line and utilizing it for various human-aid applications are more than ever. Wearable and mobile data capture devices as well as many online data channels such as search engines are all examples of means of user data collection. Such user data could be utilized to model user behavior, identify relevant information to a user and retrieve it in a timely fashion for personal assistance. User data can include recordings of one's conversations, images, biophysical data, health-related data captured by wearable devices, interactions with smartphones and computers, and more. In order to utilize such data for personal assistance, summaries of previously recorded events can be presented to a user in order to augment the user's memory, send notifications about important events to the user, predict the user's near-future information needs and retrieve relevant content even before the user asks. In this PhD dissertation, we design a personal assistant with a focus on two main aspects: The first aspect is that a personal assistant should be able to summarize user data and present it to a user. To achieve this goal, we build a Social Interactions Log Analysis System (SILAS) that summarizes a person's conversations into event snippets consisting of spoken topics paired with images and other modalities of data captured by the person's wearable devices. Furthermore, we design a novel discrete Dynamic Topic Model (dDTM) capable of tracking the evolution of the intermittent spoken topics over time. Additionally, we present the first neural Customizable Abstractive Topic-based Summarization (CATS) model that produces summaries of textual documents including meeting transcripts in the form of natural language. The second aspect that a personal assistant should be capable of, is proactively addressing the user's information needs. For this purpose, we propose a family of just-in-time information retrieval models such as an evolutionary model named Kalman combination of Recency and Establishment (K2RE) that can anticipate a user's near-future information needs. Such information needs can include information for preparing a future meeting or near-future search queries of a user
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
NEWTS: A Corpus for News Topic-Focused Summarization
Text summarization models are approaching human levels of fidelity. Existing
benchmarking corpora provide concordant pairs of full and abridged versions of
Web, news or, professional content. To date, all summarization datasets operate
under a one-size-fits-all paradigm that may not reflect the full range of
organic summarization needs. Several recently proposed models (e.g., plug and
play language models) have the capacity to condition the generated summaries on
a desired range of themes. These capacities remain largely unused and
unevaluated as there is no dedicated dataset that would support the task of
topic-focused summarization.
This paper introduces the first topical summarization corpus NEWTS, based on
the well-known CNN/Dailymail dataset, and annotated via online crowd-sourcing.
Each source article is paired with two reference summaries, each focusing on a
different theme of the source document. We evaluate a representative range of
existing techniques and analyze the effectiveness of different prompting
methods