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Generative document summarization is a natural language processing technique that generates short summary sentences while preserving the content of long texts. Various fine-tuned pre-trained document summarization models have been proposed using a specific single text-summarization dataset. However, each text-summarization dataset usually specializes in a particular downstream task. Therefore, it is difficult to treat all cases involving multiple domains using a single dataset. Accordingly, when a generative document summarization model is fine-tuned to a specific dataset, it performs well, whereas the performance is degraded by up to 45% for datasets that are not used during learning. In short, summarization models perform well with in-domain cases, as the dataset domain during training and evaluation is the same but perform poorly with out-domain inputs. In this paper, we propose a new curriculum-learning method using mixed datasets while training a generative summarization model to be more robust on out-domain datasets. Our method performed better than XSum with 10%, 20%, and 10% lower performance degradation in CNN/DM, which comprised one of two test datasets used, compared to baseline model performance. [ABSTRACT FROM AUTHOR]

Fine-tuning a pre-trained sequence-to-sequence-based language model has significantly advanced the field of abstractive summarization. However, the early models of abstractive summarization were limited by the gap between training and inference, and they did not fully utilize the potential of the language model. Recent studies have introduced a two-stage framework that allows the second-stage model to re-rank the candidate summary generated by the first-stage model, to resolve these limitations. In this study, we point out that the supervision method performed in the existing re-ranking model of the two-stage abstractive summarization framework cannot learn detailed and complex information of the data. In addition, we present the problem of positional bias in the existing encoder–decoder-based re-ranking model. To address these two limitations, this study proposes a hierarchical supervision method that jointly performs summary and sentence-level supervision. For sentence-level supervision, we designed two sentence-level loss functions: intra- and inter-intra-sentence ranking losses. Compared to the existing abstractive summarization model, the proposed method exhibited a performance improvement for both the CNN/DM and XSum datasets. The proposed model outperformed the baseline model under a few-shot setting. [ABSTRACT FROM AUTHOR]

Emotions are central to understanding contemporary journalism; however, they are overlooked in automatic news summarization. Actually, summaries are an entry point to the source article that could favor some emotions to captivate the reader. Nevertheless, the emotional content of summarization corpora and the emotional behavior of summarization models are still unexplored. In this work, we explore the usage of established methodologies to study the emotional content of summarization corpora and the emotional behavior of summarization models. Using these methodologies, we study the emotional content of two widely used summarization corpora: Cnn/Dailymail and Xsum, and the capabilities of three state-of-the-art transformer-based abstractive systems for eliciting emotions in the generated summaries: Bart, Pegasus, and T5. The main significant findings are as follows: (i) emotions are persistent in the two summarization corpora, (ii) summarizers approach moderately well the emotions of the reference summaries, and (iii) more than 75% of the emotions introduced by novel words in generated summaries are present in the reference ones. The combined use of these methodologies has allowed us to conduct a satisfactory study of the emotional content in news summarization. [ABSTRACT FROM AUTHOR]

The rapid expansion of social media platforms has resulted in an unprecedented surge of short text content being generated on a daily basis. Extracting valuable insights and patterns from this vast volume of textual data necessitates specialized techniques that can effectively condense information while preserving its core essence. In response to this challenge, automatic short text summarization (ASTS) techniques have emerged as a compelling solution, gaining significant importance in their development. This paper delves into the domain of summarizing short text on social media, exploring various types of short text and the associated challenges they present. It also investigates the approaches employed to generate concise and meaningful summaries. By providing a survey of the latest methods and potential avenues for future research, this paper contributes to the advancement of ASTS in the ever-evolving landscape of social media communication. [ABSTRACT FROM AUTHOR]

Abstractive dialogue summarization aims to generate a short passage that contains important content for a particular dialogue spoken by multiple speakers. In abstractive dialogue summarization systems, capturing the subject in the dialogue is challenging owing to the properties of colloquial texts. Moreover, the system often generates uninformative summaries. In this paper, we propose a novel keyword-aware dialogue summarization system (KADS) that easily captures the subject in the dialogue to alleviate the problem mentioned above through the efficient usage of keywords. Specifically, we first extract the keywords from the input dialogue using a pre-trained keyword extractor. Subsequently, KADS efficiently leverages the keywords information of the dialogue to the transformer-based dialogue system by using the pre-trained keyword extractor. Extensive experiments performed on three benchmark datasets show that the proposed method outperforms the baseline system. Additionally, we demonstrate that the proposed keyword-aware dialogue summarization system exhibits a high-performance gain in low-resource conditions where the number of training examples is highly limited. [ABSTRACT FROM AUTHOR]

As the popularity of large language models (LLMs) has risen over the course of the last year, led by GPT-3/4 and especially its productization as ChatGPT, we have witnessed the extensive application of LLMs to text summarization. However, LLMs do not intrinsically have the power to verify the correctness of the information they supply and generate. This research introduces a novel approach to abstractive summarization, aiming to address the limitations of LLMs in that they struggle to understand the truth. The proposed method leverages extracted knowledge graph information and structured semantics as a guide for summarization. Building upon BART, one of the state-of-the-art sequence-to-sequence pre-trained LLMs, multi-source transformer modules are developed as an encoder, which are capable of processing textual and graphical inputs. Decoding is performed based on this enriched encoding to enhance the summary quality. The Wiki-Sum dataset, derived from Wikipedia text dumps, is introduced for evaluation purposes. Comparative experiments with baseline models demonstrate the strengths of the proposed approach in generating informative and relevant summaries. We conclude by presenting our insights into utilizing LLMs with graph external information, which will become a powerful aid towards the goal of factually correct and verified LLMs. [ABSTRACT FROM AUTHOR]

Text summarization is a subtask of natural language processing referring to the automatic creation of a concise and fluent summary that captures the main ideas and topics from one or multiple documents. Earlier literature surveys focus on extractive approaches, which rank the top-n most important sentences in the input document and then combine them to form a summary. As argued in the literature, the summaries of these approaches do not have the same lexical flow or coherence as summaries that are manually produced by humans. Newer surveys elaborate abstractive approaches, which generate a summary with potentially new phrases and sentences compared to the input document. Generally speaking, contrary to the extractive approaches, the abstractive ones create summaries that are more similar to those produced by humans. However, these approaches still lack the contextual representation needed to form fluent summaries. Recent advancements in deep learning and pretrained language models led to the improvement of many natural language processing tasks, including abstractive summarization. Overall, these surveys do not present a comprehensive evaluation framework that assesses the aforementioned approaches. Taking the above into account, the contribution of this survey is fourfold: (i) we provide a comprehensive survey of the state-of-the-art approaches in text summarization; (ii) we conduct a comparative evaluation of these approaches, using well-known datasets from the related literature, as well as popular evaluation scores such as ROUGE-1, ROUGE-2, ROUGE-L, ROUGE-LSUM, BLEU-1, BLEU-2 and SACREBLEU; (iii) we report on insights gained on various aspects of the text summarization process, including existing approaches, datasets and evaluation methods, and we outline a set of open issues and future research directions; (iv) we upload the datasets and the code used in our experiments in a public repository, aiming to increase the reproducibility of this work and facilitate future research in the field. [ABSTRACT FROM AUTHOR]

Text summarization is a prominent task in natural language processing (NLP) that condenses lengthy texts into concise summaries. Despite the success of existing supervised models, they often rely on datasets of well-constructed text pairs, which can be insufficient for languages with limited annotated data, such as Chinese. To address this issue, we propose a semi-supervised learning method for text summarization. Our method is inspired by the cycle-consistent adversarial network (CycleGAN) and considers text summarization as a style transfer task. The model is trained by using a similar procedure and loss function to those of CycleGAN and learns to transfer the style of a document to its summary and vice versa. Our method can be applied to multiple languages, but this paper focuses on its performance on Chinese documents. We trained a T5-based model and evaluated it on two datasets, CSL and LCSTS, and the results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Huge quantities of audio and video material are available at universities and teaching institutions, but their use can be limited because of the lack of intelligent search tools. This paper describes a possible way to set up an indexing scheme that offers a smart search modality, that combines semantic analysis of video/audio transcripts with the exact time positioning of uttered words. The proposal leverages NLP methods for topic modeling with lexical analysis of lessons' transcripts and builds a semantic hierarchical index into the corpus of lessons analyzed. Moreover, using abstracting summarization, the system can offer short summaries on the subject semantically implied by the search carried out. [ABSTRACT FROM AUTHOR]

Recent cross-lingual summarization research has pursued the use of a unified end-to-end model which has demonstrated a certain level of improvement in performance and effectiveness, but this approach stitches together multiple tasks and makes the computation more complex. Less work has focused on alignment relationships across languages, which has led to persistent problems of summary misordering and loss of key information. For this reason, we first simplify the multitasking by converting the translation task into an equal proportion of cross-lingual summary tasks so that the model can perform only cross-lingual summary tasks when generating cross-lingual summaries. In addition, we splice monolingual and cross-lingual summary sequences as an input so that the model can fully learn the core content of the corpus. Then, we propose a reinforced regularization method based on the model to improve its robustness, and build a targeted ABO mechanism to enhance the semantic relationship alignment and key information retention of the cross-lingual summaries. Ablation experiments are conducted on three datasets of different orders of magnitude to demonstrate the effective enhancement of the model by the optimization approach; they outperform the mainstream approaches on the cross-lingual summarization task and the monolingual summarization task for the full dataset. Finally, we validate the model's capabilities on a cross-lingual summary dataset of professional domains, and the results demonstrate its superior performance and ability to improve cross-lingual sequencing. [ABSTRACT FROM AUTHOR]

Long document summarization poses obstacles to current generative transformer-based models because of the broad context to process and understand. Indeed, detecting long-range dependencies is still challenging for today's state-of-the-art solutions, usually requiring model expansion at the cost of an unsustainable demand for computing and memory capacities. This paper introduces Emma, a novel efficient memory-enhanced transformer-based architecture. By segmenting a lengthy input into multiple text fragments, our model stores and compares the current chunk with previous ones, gaining the capability to read and comprehend the entire context over the whole document with a fixed amount of GPU memory. This method enables the model to deal with theoretically infinitely long documents, using less than 18 and 13 GB of memory for training and inference, respectively. We conducted extensive performance analyses and demonstrate that Emma achieved competitive results on two datasets of different domains while consuming significantly less GPU memory than competitors do, even in low-resource settings. [ABSTRACT FROM AUTHOR]

An abstractive summarization model based on the joint-attention mechanism and a priori knowledge is proposed to address the problems of the inadequate semantic understanding of text and summaries that do not conform to human language habits in abstractive summary models. Word vectors that are most relevant to the original text should be selected first. Second, the original text is represented in two dimensions—word-level and sentence-level, as word vectors and sentence vectors, respectively. After this processing, there will be not only a relationship between word-level vectors but also a relationship between sentence-level vectors, and the decoder discriminates between word-level and sentence-level vectors based on their relationship with the hidden state of the decoder. Then, the pointer generation network is improved using a priori knowledge. Finally, reinforcement learning is used to improve the quality of the generated summaries. Experiments on two classical datasets, CNN/DailyMail and DUC 2004, show that the model has good performance and effectively improves the quality of generated summaries. [ABSTRACT FROM AUTHOR]