Top Research Papers on Text Summarization

This paper forms the extractive summarization task as a semantic text matching problem, in which a source document and candidate summaries will be matched in a semantic space to create a semantic matching framework.

This research provides a comprehensive survey for the researchers by presenting the different aspects of ATS: approaches, methods, building blocks, techniques, datasets, evaluation methods, and future research directions.

A new approach to collecting abstract data using artificial neural networks (GANs), a class of deep learning models known for their ability to create patterns of real information, shows its promise in paving the way for advanced applications in fields.

DiscoBert extracts sub-sentential discourse units (instead of sentences) as candidates for extractive selection on a finer granularity and outperforms state-of-the-art methods by a significant margin on popular summarization benchmarks compared to other BERT-base models.

Every modern text summarization approach’s workflow and significance are reviewed with the limitations with potential recovery methods, including the feature extraction approaches, datasets, performance measurement techniques, and challenges of the ATS domain, etc.

This paper provides a broad and systematic review of research in the field of text summarization published from 2008 to 2019 and describes the techniques and methods that are often used by researchers as a comparison and means for developing methods.

A set of new metrics are proposed to quantify the entity-level factual consistency of generated summaries and it is shown that the entity hallucination problem can be alleviated by simply filtering the training data.

This article provides a comprehensive literature survey on different seq2seq models for abstractive text summarization from the viewpoint of network structures, training strategies, and summary generation algorithms.

A topic-aware extractive and abstractive summarization model named T-BERTSum, based on Bidirectional Encoder Representations from Transformers (BERTs), which achieves new state-of-the-art results while generating consistent topics compared with the most advanced method.

A systematic approach is proposed that first constructs classifiers to identify helpful reviews and then classifies the sentences in the helpful reviews into six hotel features, and the sentiment polarities of sentences are analyzed to generate the review summaries.

A novel context-aware multi-view summarization network to summarize context-enhanced visual region information from multiple views and designs an adaptive gating self-attention module to extract representations of visual regions and words.

Comparative performance assessment of large language models identified ChatGPT-4 as the best-adapted model across a diverse set of clinical text summarization tasks, and it outperformed 10 medical experts in a reader study.

This paper proposes a Detect-to-Summarize network (DSNet) framework for supervised video summarization that contains anchor-based and anchor-free counterparts, and provides a dense sampling of temporal interest proposals with multi-scale intervals that accommodate interest variations in length.

We present MLSUM, the first large-scale MultiLingual SUMmarization dataset. Obtained from online newspapers, it contains 1.5M+ article/summary pairs in five different languages – namely, French, German, Spanish, Russian, Turkish. Together with English news articles from the popular CNN/Daily mail dataset, the collected data form a large scale multilingual dataset which can enable new research directions for the text summarization community. We report cross-lingual comparative analyses based on state-of-the-art systems. These highlight existing biases which motivate the use of a multi-lingual d...

It is argued that for some highly structured and recurring events, such as sports, it is better to use more sophisticated techniques to summarize the relevant tweets, and a solution based on learning the underlying hidden state representation of the event via Hidden Markov Models is given.

This paper proposes a novel framework named Document Summarization based on Data Reconstruction (DSDR), which generates a summary which consist of those sentences that can best reconstruct the original document.

A fact-aware summarization model FASum is proposed to extract and integrate factual relations into the summary generation process via graph attention and a factual corrector model FC is designed to automatically correct factual errors from summaries generated by existing systems.

ive summarization models are commonly trained using maximum likelihood estimation, which assumes a deterministic (one-point) target distribution in which an ideal model will assign all the probability mass to the reference summary. This assumption may lead to performance degradation during inference, where the model needs to compare several system-generated (candidate) summaries that have deviated from the reference summary. To address this problem, we propose a novel training paradigm which assumes a non-deterministic distribution so that different candidate summaries are assigned probability...

Hepos, a novel efficient encoder-decoder attention with head-wise positional strides to effectively pinpoint salient information from the source is proposed, able to process ten times more tokens than existing models that use full attentions.

This paper proposes a retrieval-based neural source code summarization approach where the neural model is enhanced with the most similar code snippets retrieved from the training set, and the experimental results show that the proposed approach can improve the state-of-the-art methods.