Document Summarization

: 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 …...

Though both document summarization and keyword extraction aim to extract concise representations from documents, these two tasks have usually been investigated inde- pendently. This paper proposes a novel it- erative …...

Extracting temporal information from raw text is fundamental for deep language understand- ing, and key to many applications like ques- tion answering, information extraction, and document summarization. In this paper, …...

We design a class of submodular functions meant for document summarization tasks. These functions each combine two terms, one which encourages the summary to be representative of the corpus, and …...

Document summarization is an important task in the area of natural language processing, which aims to extract the most important in- formation from a single document or a clus- ter …...

Keyphrases have found wide ranging application in NLP and IR tasks such as document sum- marization, indexing, labeling, clustering and classification. In this paper we pose the problem of extracting …...

Traditional approaches to extractive summarization rely heavily on human- engineered features. In this work we propose a data-driven approach based on neural networks and continuous sentence features. We develop a …...

AbstractThe widespread adoption of reference-based automatic evaluation metrics such as ROUGE has promoted the development of document summarization. In this paper, we consider a new protocol for designing reference-based metrics …...

AbstractEfficient document summarization requires evaluation measures that can not only rank a set of systems based on an average score, but also highlight which individual summary is better than another. …...

AbstractEvaluation of a document summarization system has been a critical factor to impact the success of the summarization task. Previous approaches, such as ROUGE, mainly consider the informativeness of the …...

AbstractTransformer-based models have achieved state-of-the-art results in a wide range of natural language processing (NLP) tasks including document summarization. Typically these systems are trained by fine-tuning a large pre-trained model …...