Multi-document Summarization with Maximal Marginal Relevance-guided Reinforcement Learning

Abstract

While neural sequence learning methods have made significant progress in single-document summarization (SDS), they produce unsatisfactory results on multi-document summarization (MDS). We observe two major challenges when adapting SDS advances to MDS: ( 1) MDS involves larger search space and yet more limited training data, setting obstacles for neural methods to learn adequate representations; (2) MDS needs to resolve higher information redundancy among the source documents, which SDS methods are less effective to handle. To close the gap, we present RL-MMR, Maximal Margin Relevance-guided Reinforcement Learning for MDS, which unifies advanced neural SDS methods and statistical measures used in classical MDS. RL-MMR casts MMR guidance on fewer promising candidates, which restrains the search space and thus leads to better representation learning. Additionally, the explicit redundancy measure in MMR helps the neural representation of the summary to better capture redundancy. Extensive experiments demonstrate that RL-MMR achieves state-of-the-art performance on benchmark MDS datasets. In particular, we show the benefits of incorporating MMR into end-to-end learning when adapting SDS to MDS in terms of both learning effectiveness and efficiency. 1

Proceedings of the Second Workshop on Scholarly Document Processing

“…BM25 and query likelihood (QL) are the most popular adhoc retrieval models for the first stage (Nogueira et al, 2019;Boudin et al, 2020;Tang and Arnold, 2020). More recently, instead of using sparse models, methods of using dense representations have been proposed (Karpukhin et al, 2020;Xiong et al, 2020;Qu et al, 2020), which can help alleviate the vocabulary mismatch problem through a dense representation space. However, recent work has revealed the limitations on their performance and efficiency (Lin, 2019;Xiong et al, 2020;Luan et al, 2020).…”

“…Query / Document Expansion Query and document expansions have been widely used in IR systems. In terms of query expansion, Jaleel et al (2004) proposed pseudo relevance feedback (RM3), which is revisited in more recent work (Dibia, 2020;Mao et al, 2020) for its strength. There are also methods that expand queries using generation schemes (Mao et al, 2020;Claveau, 2020).…”

“…Information retrieval (IR) is the task of retrieving the most relevant documents, including scientific ones (Boudin et al, 2020;Noh and Kavuluru, 2020), for a given query. IR systems have received considerable attention as they are not only required to search documents for information, but are also used as a core component in various downstream language understanding tasks such as open-domain question answering (Seo et al, 2019;Qu et al, 2020), fact verification (Thorne et al, 2018; and information extraction (Narasimhan et al, 2016;Das et al, 2020).…”

“…Along this line, there are methods that expand queries and documents with their relevant terms. They include document expansion methods (Nogueira et al, 2019;Boudin et al, 2020) that introduce additional contextrelated terms to given documents and query expansion methods (Mao et al, 2020;Claveau, 2020) that augment given queries with additional terms. By doing so, we can explicitly generate lexically richer documents or queries.…”

Monash-Summ@LongSumm 20 SciSummPip: An Unsupervised Scientific Paper Summarization Pipeline

“…Inspired by PageRank algorithm (Page et al, 1999), they consider the document as a graph where sentences are vertices and edges represent the relations between two sentences. Shortly thereafter, some researchers (Carbonell and Goldstein, 1998;Kurmi and Jain, 2014;Mao et al, 2020) involved a query-biased strategy, the Maximal Marginal Relevance (MMR) (Carbonell and Goldstein, 1998), in their summarizers. MMR tries to balance the relevance and diversity by controlling the trade-off parameter λ.…”