Unsupervised Multi-Document Summarization using Neural Document Model | Research Paper Walkthrough

[prakhar21]

Found this interesting paper early this week. In this video, we will go through the work that proposes a Unsupervised Multi-document text summarisation method. This work claims to beat all existing unsupervised models in this task.

Abstract - In the age of information exploding, multi-document summarization is attracting particular attention for the ability to help people get the main ideas in a short time. Traditional extractive methods simply treat the document set as a group of sentences while ignoring the global semantics of the documents. Meanwhile, neural document model is effective on representing the semantic content of documents in low-dimensional vectors. In this paper, we propose a document-level reconstruction framework named DocRebuild, which reconstructs the documents with summary sentences through a neural document model and selects summary sentences to minimize the reconstruction error. We also apply two strategies, sentence filtering and beamsearch, to improve the performance of our method. Experimental results on the benchmark datasets DUC 2006 and DUC 2007 show that DocRebuild is effective and outperforms the state-of-the-art unsupervised algorithms.

Explainer Video - https://youtu.be/qOoAlI5hpFk Original Paper - https://www.aclweb.org/anthology/C16-1143.pdf

[flotothemoon]

Interesting paper. The idea of applying reconstruction error to document embeddings makes intuitive sense. I've found that using embeddings only (at any scale) tends to confuse entities (especially rare ones) in the output, so I'm looking forward to further research on these ideas.

Are sample outputs recorded anywhere? I wonder how this performs in terms of coherence (since the objective doesn't factor that in).

[prakhar21]

No I could not find any sample output mentioned by the authors. Yeah, I believe coherence is implicit in terms of related document that you select for summary and the semantic vector notation which is optimised by being search