RankSum An unsupervised extractive text summarization based on rank
  fusion
A. Joshi E. Fidalgo E. Alegre R. Alaiz-Rodriguez
In this paper, we propose Ranksum, an approach for extractive text
summarization of single documents based on the rank fusion of four
multi-dimensional sentence features extracted for each sentence: topic
information, semantic content, significant keywords, and position. The Ranksum
obtains the sentence saliency rankings corresponding to each feature in an
unsupervised way followed by the weighted fusion of the four scores to rank the
sentences according to their significance. The scores are generated in
completely unsupervised way, and a labeled document set is required to learn
the fusion weights. Since we found that the fusion weights can generalize to
other datasets, we consider the Ranksum as an unsupervised approach. To
determine topic rank, we employ probabilistic topic models whereas semantic
information is captured using sentence embeddings. To derive rankings using
sentence embeddings, we utilize Siamese networks to produce abstractive
sentence representation and then we formulate a novel strategy to arrange them
in their order of importance. A graph-based strategy is applied to find the
significant keywords and related sentence rankings in the document. We also
formulate a sentence novelty measure based on bigrams, trigrams, and sentence
embeddings to eliminate redundant sentences from the summary. The ranks of all
the sentences computed for each feature are finally fused to get the final
score for each sentence in the document. We evaluate our approach on publicly
available summarization datasets CNN/DailyMail and DUC 2002. Experimental
results show that our approach outperforms other existing state-of-the-art
summarization methods.