Neural-based summarization models suffer from the length limitation of text encoder . Long documents have to been truncated before they are sent to the model , which results in huge loss of summary-relevant contents . To address this issue , we propose the sliding selector network with dynamic memory for extractive summarization of long-form documents , which employs a sliding window to extract summary sentences segment by segment . Moreover , we adopt memory mechanism to preserve and update the history information dynamically , allowing the semantic flow across different windows . Experimental results on two large-scale datasets that consist of scientific papers demonstrate that our model substantially outperforms previous state-of-the-art models . Besides , we perform qualitative and quantitative investigations on how our model works and where the performance gain comes from . 1 Text summarization is an important task of natural language processing which aims to distil salient contents from a textual document . Existing summarization models can be roughly classified into two categories , which are abstractive and extractive . Abstractive summarization usually adopts natural language generation technology to produce a wordby-word summary . In general , these approaches are flexible but may yield disfluent summaries ( Liu and Lapata , 2019a ) . By comparison , extractive approaches aim to select a subset of the sentences in the source document , thereby enjoying better fluency and efficiency ( Cao et al . , 2017 ) . Although many summarization approaches have demonstrated their success on relatively short documents , such as news articles , they usually fail Paragraph 1 : Medical tourism is illustrated as occurrence in which individuals travel abroad to receive healthcare services . It is a multibillion dollar industry and countries like India , Thailand , Israel , Singapore , … Paragraph 2 : The prime driving factors in medical tourism are increased medical costs , increased insurance premiums , increasing number of uninsured or partially insured individuals in developed countries , … … … Paragraph 5 : It is generally presumed in marketing that products with similar characteristics will be equally preferred by the consumers , however , attributes , which make the product similar to other products , will not … . to achieve desired performance when directly applied in long-form documents , such as scientific papers . This inferior performance is partly due to the truncation operation , which inevitably leads to information loss , especially for extractive models because parts of gold sentences would be inaccessible . In addition , the accurate modeling of long texts remains a challenge ( Frermann and Klementiev , 2019 ) . A practical solution for this problem is to use a sliding window to process documents separately . This approach is used in other NLP tasks , such as machine reading comprehension ( Wang et al . , 2019b ) . However , such a paradigm is not suitable for summarization task because the concatenation of summaries that are independently extracted from local contexts is usually inconsistent with the gold summary of the entire document . Figure 1 shows an example to illustrate this problem . The core topic of the source document is " medical tourism , " which is discussed in Paragraphs 1 and 2 . How-ever , the 5-th paragraph is mainly about " consumer and product . " As a consequence , the paragraphby-paragraph extraction approach might produce a both repetitive and noisy summary . Under this circumstance , the supervised signals will have a negative effect on model behaviors because understanding why Paragraph 5 should output an empty result without information conveying from previous texts is confused for the model . In this paper , we propose a novel extractive summarization model for long-form documents . We split the input document into multiple windows and encode them with a sliding encoder sequentially . During this process , we introduce a memory to preserve salient information learned from previous windows , which is used to complete and enrich local texts . Intuitively , our model has the following advantages : 1 ) In each window , the text encoder processes a relatively short segment , thereby yielding more accurate representations . 2 ) The local text representations can capture beyond-window contextual information via the memory module . 3 ) The previous selection results are also parameterized in the memory block , allowing the collaboration among summary sentences . To sum up , our contributions are threefold . ( 1 ) We propose a novel extractive summarization model that can summarize documents of arbitrary length without truncation loss . Also , it employs the memory mechanism to address context fragmentation . To the best of our knowledge , we are the first to propose applying memory networks into extractive text summarization task . ( 2 ) The proposed framework ( i.e. , a sliding encoder combined with dynamic memory ) provides a general solution for summarizing long documents and can be easily extended to other abstractive and extractive summarization models . ( 3 ) Our model achieves the state-of-the-art results on two widely used datasets for long document summarization . Moreover , we conduct extensive analysis to understand how our model works and where the performance gain comes from . In this study , we propose a novel extractive summarization that can summarize long-form documents without content loss . We conduct extensive experiments on two well-studied datasets that consist of scientific papers . Experimental results demonstrate that our model outperforms previous stateof-the-art models . In the future , we will extend our framework ( i.e. , a sliding encoder combined with long-range memory modeling ) to abstractive summarization models .

Challenge: Neural-based summarization models truncate documents because of the length limitation leading to a loss of summary-relevant contents and the sliding window approach is not suitable.
Approach: They propose to employ a sliding window to extract summaries segment by segment with a memory mechanism for preserving and updating the history information dynamically.
Outcome: The proposed model achieves state-of-the-art on two large-scale long document summarization datasets based on scientific papers.