Add to ORKGThe main focus of this work is to investi- gate robust ways for generating summaries from summary representations without recurring to simple sentence extraction and aiming at more human-like summaries. This is motivated by empirical evidence from TAC 2009 data showing that human summaries contain on average more and shorter sentences than the system summaries. We report encouraging preliminary results comparable to those attained by participating systems at TAC 2009
The need to access the essential content of documents accurately in order to satisfy users ' de...
A story summarizer benefits greatly from a reader model because a reader model enables the story sum...
Automated multi-document extractive text summarization is a widely studied research problem in the f...
The main focus of this work is to investigate robust ways for generating summaries from summary repr...
this paper, we present a system for summarizing quantitative data in natural language, focusing on h...
We present sentence enhancement as a novel technique for text-to-text genera-tion in abstractive sum...
This paper is based on work being carried out jointly with Jacques Robin, in the case of STREAK, and...
The need for text summarization is crucial as we enter the era of information overload. In this pape...
AbstractWe present a system for summarizing quantitative data in natural language, focusing on the u...
Human-quality text summarization systems are difficult to design, and even more difficult to evaluat...
Automatic summarization has advanced greatly in the past few decades. However, there remains a huge ...
In recent years, there has been increased interest in topic-focused multi-document summarization. In...
This work proposes an approach to address automatic text summarization. This approach is a trainable...
We define the problem of decomposing human-written summary sentences and propose a novel Hidden Mark...
Like humans, document summarization models can interpret a document’s contents in a number of ways. ...
The need to access the essential content of documents accurately in order to satisfy users ' de...
A story summarizer benefits greatly from a reader model because a reader model enables the story sum...
Automated multi-document extractive text summarization is a widely studied research problem in the f...
The main focus of this work is to investigate robust ways for generating summaries from summary repr...
this paper, we present a system for summarizing quantitative data in natural language, focusing on h...
We present sentence enhancement as a novel technique for text-to-text genera-tion in abstractive sum...
This paper is based on work being carried out jointly with Jacques Robin, in the case of STREAK, and...
The need for text summarization is crucial as we enter the era of information overload. In this pape...
AbstractWe present a system for summarizing quantitative data in natural language, focusing on the u...
Human-quality text summarization systems are difficult to design, and even more difficult to evaluat...
Automatic summarization has advanced greatly in the past few decades. However, there remains a huge ...
In recent years, there has been increased interest in topic-focused multi-document summarization. In...
This work proposes an approach to address automatic text summarization. This approach is a trainable...
We define the problem of decomposing human-written summary sentences and propose a novel Hidden Mark...
Like humans, document summarization models can interpret a document’s contents in a number of ways. ...
The need to access the essential content of documents accurately in order to satisfy users ' de...
A story summarizer benefits greatly from a reader model because a reader model enables the story sum...
Automated multi-document extractive text summarization is a widely studied research problem in the f...