Our team submitted runs for the first running of the TREC Temporal Summarization track. We focused on the Sequential Update Summarization task. This task involves simulating processing a temporally ordered stream of over 1 billion documents to identify sentences that are relevant to a specific breaking news stories which contain new and important content. In this paper, we describe our approach...

META: Multi-resolution Framework for Event Summarization Report Title Event summarization is an effective process that mines and organizes event patterns to represent the original events. It allows the analysts to quickly gain the general idea of the events. In recent years, several event summarization algorithms have been proposed, but they all focus on how to find out the optimal summarizatio...

Due to its promise to alleviate information overload, text summarization has attracted the attention of many researchers. However, it has remained a serious challenge. Here, we first prove empirical limits on the recall (and F1scores) of extractive summarizers on the DUC datasets under ROUGE evaluation for both the single-document and multi-document summarization tasks. Next we define the conce...

The increasing availability of online information has necessitated intensive research in the area of automatic text summarization within the Natural Language Processing (NLP) community. Over the past half a century, the problem has been addressed from many different perspectives, in varying domains and using various paradigms. This survey intends to investigate some of the most relevant approac...

Text summarization aims to reduce text by removing less useful information obtain quickly and precisely. In Indonesian abstractive summarization, the research mostly focuses on multi-document which methods will not work optimally in single-document summarization. As public datasets works English are focusing this study emphasized Abstractive studies frequently use Bidirectional Encoder Represen...

In this paper, we consider the automatic text summarization as a challenging task of machine learning. We proposed a novel summarization system architecture which employs Gene Expression Programming technique as its learning mechanism. The preliminary experimental results have shown that our prototype system outperforms the baseline systems.

In this paper, we present a combination of a multi-document summarization system with a genetic algorithm. We first introduce a novel approach for automatic summarization. CBSEAS, the system which implements this approach, integrates a new method to detect redundancy at its very core in order to produce summaries with a good informational diversity. However, the evaluation of our system at TAC ...

News production, delivery, and consumption are increasing in ubiquity and speed, spreading over more software and hardware platforms, in particular mobile devices. This has led to an increasing interest in automated methods for multi-document summarization. We start this chapter with discussing several new alternatives for automated news summarization, with a particular focus on temporal text m...