Summarisation of research results in plain language is crucial for promoting
public understanding of research findings. The use of Natural Language
Processing to generate lay summaries has the potential to relieve researchers'
workload and bridge the gap between science and society. The aim of this
narrative literature review is to describe and compare the different text
summarisation approaches used to generate lay summaries. We searched the
databases Web of Science, Google Scholar, IEEE Xplore, Association for
Computing Machinery Digital Library and arXiv for articles published until 6
May 2022. We included original studies on automatic text summarisation methods
to generate lay summaries. We screened 82 articles and included eight relevant
papers published between 2020 and 2021, all using the same dataset. The results
show that transformer-based methods such as Bidirectional Encoder
Representations from Transformers (BERT) and Pre-training with Extracted
Gap-sentences for Abstractive Summarization (PEGASUS) dominate the landscape of
lay text summarisation, with all but one study using these methods. A
combination of extractive and abstractive summarisation methods in a hybrid
approach was found to be most effective. Furthermore, pre-processing approaches
to input text (e.g. applying extractive summarisation) or determining which
sections of a text to include, appear critical. Evaluation metrics such as
Recall-Oriented Understudy for Gisting Evaluation (ROUGE) were used, which do
not consider readability. To conclude, automatic lay text summarisation is
under-explored. Future research should consider long document lay text
summarisation, including clinical trial reports, and the development of
evaluation metrics that consider readability of the lay summary.

本文是一篇关于使用自然语言处理技术生成简化版摘要的研究综述，发现基于 transformer 的方法如 BERT 和 PEGASUS 在生成 lay text summarisation 方面相对优秀，并建议采用抽取式和生成式相结合的混合方法，并开发一些新的评估指标以确保 lay summary 更易读。

利用自然语言处理实现文本摘要：叙事性文献综述

Lay Text Summarisation Using Natural Language Processing: A Narrative Literature Review

This paper provides results of evaluating some text summarisation techniques
for the purpose of producing call summaries for contact centre solutions. We
specifically focus on extractive summarisation methods, as they do not require
any labelled data and are fairly quick and easy to implement for production
use. We experimentally compare several such methods by using them to produce
summaries of calls, and evaluating these summaries objectively (using ROUGE-L)
and subjectively (by aggregating the judgements of several annotators). We
found that TopicSum and Lead-N outperform the other summarisation methods,
whilst BERTSum received comparatively lower scores in both subjective and
objective evaluations. The results demonstrate that even such simple
heuristics-based methods like Lead-N ca n produce meaningful and useful
summaries of call centre dialogues.

本研究评估了一些文本摘要技术，以产生呼叫总结。实验结果表明，采用 TopicSum 和 Lead-N 方法的摘要优于其他方法，而 BERTSum 在主观和客观评估中得分相对较低。结果表明，即使像 Lead-N 这样简单的启发式方法也可以生成有意义和有用的呼叫中心对话摘要。

比较呼叫中心对话的摘要提取方法

Comparing Methods for Extractive Summarization of Call Centre Dialogue

Supervised approaches for text summarisation suffer from the problem of
mismatch between the target labels/scores of individual sentences and the
evaluation score of the final summary. Reinforcement learning can solve this
problem by providing a learning mechanism that uses the score of the final
summary as a guide to determine the decisions made at the time of selection of
each sentence. In this paper we present a proof-of-concept approach that
applies a policy-gradient algorithm to learn a stochastic policy using an
undiscounted reward. The method has been applied to a policy consisting of a
simple neural network and simple features. The resulting deep reinforcement
learning system is able to learn a global policy and obtain encouraging
results.

本文提出一种应用基于策略梯度算法的深度强化学习系统的证明性方法，以解决文本摘要中标签不匹配问题，使用无折扣奖励学习具有简单神经网络和简单特征的策略，验证结果表明在全局策略下具有鼓舞人心的表现。