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Importance evaluation is one of the most challenging problems in the field of text processing. In the paper we focus on the notion of importance from a computational standpoint, and …...

This paper addresses the problem of iden- tifying likely topics of texts by their posi- tion in the text. It describes the automated training and evaluation of an Optimal Posi- …...

: This paper 1 develops a method for combining query- relevance with information-novelty in the context of text retrieval and summarization. The Maximal Marginal Relevance (MMR) criterion strives to reduce …...

We describe and evaluate an implemented system for general-knowledge question answering. The sys- tem combines techniques for standard ad-hoc infor- mation retrieval (IR), query-dependent text summa- rization, and shallow syntactic …...

Automatic text categorization is a problem of automatically assigning text documents to predefined categories. In order to classify text documents, we must extract good features from them. In previous research, …...

Extracting sentences that contain important in- formation from a document is a form of text summarization. The technique is the key to the automatic generation of summaries similar to those …...

The discovery of semantic relations from text becomes increasingly important for applica- tions such as Question Answering, Informa- tion Extraction, Text Summarization, Text Un- derstanding, and others. The semantic rela- …...

Word segmentation is an important part of many applications, including information retrieval, information filtering, document analysis, and text summarization. In Thai language, the process is complicated since words are written …...

The paper presents a Bayesian model for text summarization, which explicitly en- codes and exploits information on how hu- man judgments are distributed over the text. Comparison is made against …...

We have explored the usefulness of incorporat- ing speech and discourse features in an automatic speech summarization system applied to meeting recordings from the ICSI Meetings corpus. By an- alyzing …...

One of the main challenges in the applica- tions (i.e.: text summarization, question an- swering, information retrieval, etc.) of Natural Language Processing is to deter- mine which of the several …...

AbstractWe describe ongoing work in semi-automatic annotating corpus, with the goal to answer why-question in question answering system and give a construction of the coherent tree for text summarization. In …...

. In this paper, we discuss the summarization for supporting a user’s judgment on the credibility of information on the Web. In general, if a statement contradicts another statement, the …...

We discuss text summarization in terms of maximum coverage problem and its vari- ant. We explore some decoding algorithms including the ones never used in this sum- marization formulation, such …...

We treat the text summarization problem as maximizing a submodular function under a budget constraint. We show, both theoretically and empirically, a modified greedy algorithm can efficiently solve the budgeted …...

Text summarization solves the problem of extracting important information from huge amount of text data. There are vari- ous methods in the literature that aim to find out well-formed summaries. …...

Graph-based methods have gained attention in many areas of Natural Language Processing (NLP) including Word Sense Disambiguation (WSD), text summarization, keyword extrac- tion and others. Most of the work in …...

We present a new method to generate extractive multi-document summaries. The method uses Integer Linear Programming to jointly maximize the importance of the sentences it includes in the summary and …...

AbstractParaphrases are alternative syntactic forms in the same language expressing the same semantic content. Speakers of all languages are inherently familiar with paraphrases at different levels of granularity (lexical, phrasal, …...

This study proposes a text summarization model that simultaneously performs sen- tence extraction and compression. We translate the text summarization task into a problem of extracting a set of depen- …...

Lexical Cohesion is a commonly studied lin- guistic feature as it is easily identified from the surface of a text. However, the purposes for studying lexical cohesion are varied, and …...

. Keyphrases are single or multi-word expressions that represent the main content of a document. Key- phrases are useful in many applications such as document indexing or text summarization. However, …...

Neural encoder-decoder models have shown great success in many sequence generation tasks. However, previous work has not in- vestigated situations in which we would like to control the length of …...

We address an important problem in sequence-to-sequence (Seq2Seq) learning referred to as copying, in which cer- tain segments in the input sequence are selectively replicated in the output se- quence. …...

The problem of rare and unknown words is an important issue that can potentially effect the performance of many NLP sys- tems, including traditional count-based and deep learning models. We …...

AbstractWord Sense Disambiguation (WSD) is one of the open problems in the area of natural language processing. Various supervised, unsupervised and knowledge based approaches have been proposed for automatically determining …...

AbstractWe present bot#1337: a dialog system developed for the 1st NIPS Conversational Intelligence Challenge 2017 (ConvAI). The aim of the competition was to implement a bot capable of conversing with …...

AbstractText summarization is considered as a challenging task in the NLP community. The availability of datasets for the task of multilingual text summarization is rare, and such datasets are difficult …...

AbstractUnder special circumstances, summaries should conform to a particular style with patterns, such as court judgments and abstracts in academic papers. To this end, the prototype document-summary pairs can be …...

AbstractWe introduce the Scratchpad Mechanism, a novel addition to the sequence-to-sequence (seq2seq) neural network architecture and demonstrate its effectiveness in improving the overall fluency of seq2seq models for natural language …...

AbstractAuthors’ keyphrases assigned to scientific articles are essential for recognizing content and topic aspects. Most of the proposed supervised and unsupervised methods for keyphrase generation are unable to produce terms …...

AbstractThe recent years have seen remarkable success in the use of deep neural networks on text summarization. However, there is no clear understanding of why they perform so well, or …...

AbstractGiven the peculiar structure of songs, applying generic text summarization methods to lyrics can lead to the generation of highly redundant and incoherent text. In this paper, we propose to …...

AbstractBidirectional Encoder Representations from Transformers (BERT) represents the latest incarnation of pretrained language models which have recently advanced a wide range of natural language processing tasks. In this paper, we …...

Text summarization is an important yet chal- lenging task in natural language processing. In this paper, we investigate Pointer Genera- tor Networks for sentence compression. Using Vietnamese as a case …...

AbstractSequence-to-sequence (seq2seq) network is a well-established model for text summarization task. It can learn to produce readable content; however, it falls short in effectively identifying key regions of the source. …...

AbstractNeural network-based models augmented with unsupervised pre-trained knowledge have achieved impressive performance on text summarization. However, most existing evaluation methods are limited to an in-domain setting, where summarizers are trained …...

AbstractMuch progress has been made in text summarization, fueled by neural architectures using large-scale training corpora. However, in the news domain, neural models easily overfit by leveraging position-related features due …...

AbstractFact verification models have enjoyed a fast advancement in the last two years with the development of pre-trained language models like BERT and the release of large scale datasets such …...

AbstractEntities are the major proportion and build up the topic of text summaries. Although existing text summarization models can produce promising results of automatic metrics, for example, ROUGE, it is …...

AbstractText summarization aims to compress a textual document to a short summary while keeping salient information. Extractive approaches are widely used in text summarization because of their fluency and efficiency. …...

AbstractCode-mixing is a phenomenon which arises mainly in multilingual societies. Multilingual people, who are well versed in their native languages and also English speakers, tend to code-mix using English-based phonetic …...

AbstractThe goal of text summarization is to compress documents to the relevant information while excluding background information already known to the receiver. So far, summarization researchers have given considerably more …...

AbstractWe propose a family of metrics to assess language generation derived from population estimation methods widely used in ecology. More specifically, we use mark-recapture and maximum-likelihood methods that have been …...

AbstractWe 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 …...

AbstractText summarization is one of the most challenging and interesting problems in NLP. Although much attention has been paid to summarizing structured text like news reports or encyclopedia articles, summarizing …...

AbstractAutomated evaluation metrics as a stand-in for manual evaluation are an essential part of the development of text-generation tasks such as text summarization. However, while the field has progressed, our …...

AbstractText summarization refers to the process that generates a shorter form of text from the source document preserving salient information. Many existing works for text summarization are generally evaluated by …...

AbstractRecent developments in sequence-to-sequence learning with neural networks have considerably improved the quality of automatically generated text summaries and document keywords, stipulating the need for even bigger training corpora. Metadata …...

AbstractDeep learning has led to significant improvement in text summarization with various methods investigated and improved ROUGE scores reported over the years. However, gaps still exist between summaries produced by …...

AbstractExtracting the most important part of legislation documents has great business value because the texts are usually very long and hard to understand. The aim of this article is to …...

Journalists usually organize and present the contents of a news article following a well- defined structure. In this paper, we propose a novel joint model for structure-based news genre classification …...

AbstractAlthough parsing to Abstract Meaning Representation (AMR) has become very popular and AMR has been shown effective on the many sentence-level downstream tasks, little work has studied how to generate …...

This work empirically explores effective ex- ploiting of intermediate output from pre- trained language models (PrLMs) for language generation tasks. For this purpose, we propose an improved method to integrate …...

AbstractSentence fusion is a conditional generation task that merges several related sentences into a coherent one, which can be deemed as a summary sentence. The importance of sentence fusion has …...

AbstractTransformer is an attention-based neural network, which consists of two sublayers, namely, Self-Attention Network (SAN) and Feed-Forward Network (FFN). Existing research explores to enhance the two sublayers separately to improve …...

AbstractIn this paper we apply self-knowledge distillation to text summarization which we argue can alleviate problems with maximum-likelihood training on single reference and noisy datasets. Instead of relying on one-hot …...

AbstractNon-autoregressive generation (NAG) has recently attracted great attention due to its fast inference speed. However, the generation quality of existing NAG models still lags behind their autoregressive counterparts. In this …...

AbstractThe multimodality problem has become a major challenge of existing non-autoregressive generation (NAG) systems. A common solution often resorts to sequence-level knowledge distillation by rebuilding the training dataset through autoregressive …...

AbstractAlthough some recent works show potential complementarity among different state-of-the-art systems, few works try to investigate this problem in text summarization. Researchers in other areas commonly refer to the techniques …...

AbstractScheduled sampling is widely used to mitigate the exposure bias problem for neural machine translation. Its core motivation is to simulate the inference scene during training by replacing ground-truth tokens …...

AbstractThe scarcity of comprehensive up-to-date studies on evaluation metrics for text summarization and the lack of consensus regarding evaluation protocols continue to inhibit progress. We address the existing shortcomings of …...

AbstractText augmentation is an effective technique in alleviating overfitting in NLP tasks. In existing methods, text augmentation and downstream tasks are mostly performed separately. As a result, the augmented texts …...

AbstractPre-trained models have brought remarkable success on the text summarization task. For dialogue summarization, the subdomain of text summarization, utterances are concatenated to flat text before being processed. As a …...

AbstractAutomatic text summarization systems commonly involve humans for preparing data or evaluating model performance, yet, there lacks a systematic understanding of humans’ roles, experience, and needs when interacting with or …...

AbstractText 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 …...

AbstractGenerative Pre-trained Transformers (GPTs) have recently been scaled to unprecedented sizes in the history of machine learning. These models, solely trained on the language modeling objective, have been shown to …...

AbstractNatural language modeling with limited training data is a challenging problem, and many algorithms make use of large-scale pretrained language models (PLMs) for this due to its great generalization ability. …...

AbstractText summarization helps readers capture salient information from documents, news, interviews, and meetings. However, most state-of-the-art pretrained language models (LM) are unable to efficiently process long text for many summarization …...

AbstractMulti-dimensional evaluation is the dominant paradigm for human evaluation in Natural Language Generation (NLG), i.e., evaluating the generated text from multiple explainable dimensions, such as coherence and fluency. However, automatic …...

AbstractRecent work has identified noisy and misannotated data as a core cause of hallucinations and unfaithful outputs in Natural Language Generation (NLG) tasks. Consequently, identifying and removing these examples is …...

AbstractExisting evaluation metrics for natural language generation (NLG) tasks face the challenges on generalization ability and interpretability. Specifically, most of the well-performed metrics are required to train on evaluation datasets …...

AbstractAutomated evaluation of text generation systems has recently seen increasing attention, particularly checking whether generated text stays truthful to input sources. Existing methods frequently rely on an evaluation using task-specific …...

AbstractText summarization aims to condense long documents and retain key information. Critical to the success of a summarization model is the faithful inference of latent representations of words or tokens …...

AbstractIn this study, we analyze automatic evaluation metrics for Natural Language Generation (NLG), specifically task-agnostic metrics and human-aligned metrics. Task-agnostic metrics, such as Perplexity, BLEU, BERTScore, are cost-effective and highly …...