Question: What do they use to match highlights to document content?
Evidence: The need to access and digest large amounts of textual data has provided strong impetus to develop automatic summarization systems aiming to create shorter versions of one or more documents, whilst preserving their information content. Much effort in automatic summarization has been devoted to sentence extraction, where a summary is created by identifying and subsequently concatenating the most salient text units in a document.
Most extractive methods to date identify sentences based on human-engineered features. These include surface features such as sentence position and length BIBREF0 , the words in the title, the presence of proper nouns, content features such as word frequency BIBREF1 , and event features such as action nouns BIBREF2 . Sentences are typically assigned a score indicating the strength of presence of these features. Several methods have been used in order to select the summary sentences ranging from binary classifiers BIBREF3 , to hidden Markov models BIBREF4 , graph-based algorithms BIBREF5 , BIBREF6 , and integer linear programming BIBREF7 .
In this work we propose a data-driven approach to summarization based on neural networks and continuous sentence features. There has been a surge of interest recently in repurposing sequence transduction neural network architectures for NLP tasks such as machine translation BIBREF8 , question answering BIBREF9 , and sentence compression BIBREF10 . Central to these approaches is an encoder-decoder architecture modeled by recurrent neural networks. The encoder reads the source sequence into a list of continuous-space representations from which the decoder generates the target sequence. An attention mechanism BIBREF11 is often used to locate the region of focus during decoding.
We develop a general framework for single-document summarization which can be used to extract sentences or words. Our model includes a neural network-based hierarchical document reader or encoder and an attention-based content extractor. The role of the reader is to derive the meaning representation of a document based on its sentences and their constituent words. Our models adopt a variant of neural attention to extract sentences or words. Contrary to previous work where attention is an intermediate step used to blend hidden units of an encoder to a vector propagating additional information to the decoder, our model applies attention directly to select sentences or words of the input document as the output summary. Similar neural attention architectures have been previously used for geometry reasoning BIBREF12 , under the name Pointer Networks.
One stumbling block to applying neural network models to extractive summarization is the lack of training data, i.e., documents with sentences (and words) labeled as summary-worthy. Inspired by previous work on summarization BIBREF7 , BIBREF13 and reading comprehension BIBREF9 we retrieve hundreds of thousands of news articles and corresponding highlights from the DailyMail website. Highlights usually appear as bullet points giving a brief overview of the information contained in the article (see Figure 1 for an example). Using a number of transformation and scoring algorithms, we are able to match highlights to document content and construct two large scale training datasets, one for sentence extraction and the other for word extraction. Previous approaches have used small scale training data in the range of a few hundred examples.
Our work touches on several strands of research within summarization and neural sequence modeling. The idea of creating a summary by extracting words from the source document was pioneered in bankoetal00 who view summarization as a problem analogous to statistical machine translation and generate headlines using statistical models for selecting and ordering the summary words. Our word-based model is similar in spirit, however, it operates over continuous representations, produces multi-sentence output, and jointly selects summary words and organizes them into sentences. A few recent studies BIBREF14 , BIBREF15 perform sentence extraction based on pre-trained sentence embeddings following an unsupervised optimization paradigm. Our work also uses continuous representations to express the meaning of sentences and documents, but importantly employs neural networks more directly to perform the actual summarization task.
rush2015neural propose a neural attention model for abstractive sentence compression which is trained on pairs of headlines and first sentences in an article. In contrast, our model summarizes documents rather than individual sentences, producing multi-sentential discourse. A major architectural difference is that our decoder selects output symbols from the document of interest rather than the entire vocabulary. This effectively helps us sidestep the difficulty of searching for the next output symbol under a large vocabulary, with low-frequency words and named entities whose representations can be challenging to learn. Gu:ea:16 and gulcehre2016pointing propose a similar “copy” mechanism in sentence compression and other tasks; their model can accommodate both generation and extraction by selecting which sub-sequences in the input sequence to copy in the output.
We evaluate our models both automatically (in terms of Rouge) and by humans on two datasets: the benchmark DUC 2002 document summarization corpus and our own DailyMail news highlights corpus. Experimental results show that our summarizers achieve performance comparable to state-of-the-art systems employing hand-engineered features and sophisticated linguistic constraints.
Using a number of transformation and scoring algorithms, we are able to match highlights to document content and construct two large scale training datasets, one for sentence extraction and the other for word extraction.
Answer: A number of transformation and scoring algorithms.