In this paper, we explore the use of automatic syntactic simplification for improving content selection in multi-document summarization. In particular, we show how simplifying parentheticals by removing relative clauses and appositives results in improved sentence clustering, by forcing clustering based on central rather than background information. We argue that the inclusion of parenthetical information in a summary is a reference-generation task rather than a content-selection one, and implement a baseline reference rewriting module. We perform our evaluations on the test sets from the 2003 and 2004 Document Understanding Conference and report that simplifying parentheticals results in significant improvement on the automated evaluation ...
In this paper we explore the benefits from and shortcomings of entity-driven noun phrase rewriting f...
In this thesis, we have approached a technique for tackling abstractive text summarization tasks wi...
Abstract Automatic text summarization is a dynamic area in Natural Language Processing that has g...
In this paper, we explore the use of automatic syntactic simplification for improving content select...
References included in multi-document summaries are often problematic. In this paper, we present a c...
We present a statistical similarity measuring and clustering tool, SIMFINDER, that organizes small p...
Text clustering methods were traditionally incorporated into multi-document summarization (MDS) as a...
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...
In recent years, there has been increased interest in topic-focused multi-document summarization. In...
This paper presents a method for extractive multi-document summarization that explores a two-phase c...
In this paper we explore the benefits from and shortcomings of entity-driven noun phrase rewriting f...
Automatic summarization has advanced greatly in the past few decades. However, there remains a huge ...
Automatic multi-document summarization needs to find representative sentences not only by sentence d...
We show that by making use of information common to document sets belonging to a common category, we...
In this paper we explore the benefits from and shortcomings of entity-driven noun phrase rewriting f...
In this thesis, we have approached a technique for tackling abstractive text summarization tasks wi...
Abstract Automatic text summarization is a dynamic area in Natural Language Processing that has g...
In this paper, we explore the use of automatic syntactic simplification for improving content select...
References included in multi-document summaries are often problematic. In this paper, we present a c...
We present a statistical similarity measuring and clustering tool, SIMFINDER, that organizes small p...
Text clustering methods were traditionally incorporated into multi-document summarization (MDS) as a...
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...
In recent years, there has been increased interest in topic-focused multi-document summarization. In...
This paper presents a method for extractive multi-document summarization that explores a two-phase c...
In this paper we explore the benefits from and shortcomings of entity-driven noun phrase rewriting f...
Automatic summarization has advanced greatly in the past few decades. However, there remains a huge ...
Automatic multi-document summarization needs to find representative sentences not only by sentence d...
We show that by making use of information common to document sets belonging to a common category, we...
In this paper we explore the benefits from and shortcomings of entity-driven noun phrase rewriting f...
In this thesis, we have approached a technique for tackling abstractive text summarization tasks wi...
Abstract Automatic text summarization is a dynamic area in Natural Language Processing that has g...