Reader-Aware Multi-Document Summarization via Sparse Coding

04/28/2015
by   Piji Li, et al.
0

We propose a new MDS paradigm called reader-aware multi-document summarization (RA-MDS). Specifically, a set of reader comments associated with the news reports are also collected. The generated summaries from the reports for the event should be salient according to not only the reports but also the reader comments. To tackle this RA-MDS problem, we propose a sparse-coding-based method that is able to calculate the salience of the text units by jointly considering news reports and reader comments. Another reader-aware characteristic of our framework is to improve linguistic quality via entity rewriting. The rewriting consideration is jointly assessed together with other summarization requirements under a unified optimization model. To support the generation of compressive summaries via optimization, we explore a finer syntactic unit, namely, noun/verb phrase. In this work, we also generate a data set for conducting RA-MDS. Extensive experiments on this data set and some classical data sets demonstrate the effectiveness of our proposed approach.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
08/03/2017

Reader-Aware Multi-Document Summarization: An Enhanced Model and The First Dataset

We investigate the problem of reader-aware multi-document summarization ...
research
09/13/2022

Document-aware Positional Encoding and Linguistic-guided Encoding for Abstractive Multi-document Summarization

One key challenge in multi-document summarization is to capture the rela...
research
10/21/2021

Topic-Guided Abstractive Multi-Document Summarization

A critical point of multi-document summarization (MDS) is to learn the r...
research
03/03/2022

PeerSum: A Peer Review Dataset for Abstractive Multi-document Summarization

We present PeerSum, a new MDS dataset using peer reviews of scientific p...
research
12/13/2018

Abstractive Text Summarization by Incorporating Reader Comments

In neural abstractive summarization field, conventional sequence-to-sequ...
research
05/19/2021

Analysis of GraphSum's Attention Weights to Improve the Explainability of Multi-Document Summarization

Modern multi-document summarization (MDS) methods are based on transform...
research
10/18/2018

A Temporally Sensitive Submodularity Framework for Timeline Summarization

Timeline summarization (TLS) creates an overview of long-running events ...

Please sign up or login with your details

Forgot password? Click here to reset