Question: What is the two-step process for extracting multiple summary graphs?
Evidence: Summarization of large texts is still an open problem in language processing. People nowadays have lesser time and patience to go through large pieces of text which make automatic summarization important. Automatic summarization has significant applications in summarizing large texts like stories, journal papers, news articles and even larger texts like books.
Existing methods for summarization can be broadly categorized into two categories Extractive and Abstractive. Extractive methods picks up words and sometimes directly sentences from the text. These methods are inherently limited in the sense that they can never generate human level summaries for large and complicated documents which require rephrasing sentences and incorporating information from full text to generate summaries. Most of the work done on summarization in past has been extractive.
On the other hand most Abstractive methods take advantages of the recent developments in deep learning. Specifically the recent success of the sequence to sequence learning models where recurrent networks read the text, encodes it and then generate target text. Though these methods have recently shown to be competitive with the extractive methods they are still far away from reaching human level quality in summary generation.
The work on summarization using AMR was started by BIBREF0 . Abstract Meaning Representation (AMR) was as introduced by BIBREF1 . AMR focuses on capturing the meaning of the text, by giving a specific meaning representation to the text. AMR tries to capture the "who is doing what to whom" in a sentence. The formalism aims to give same representation to sentences which have the same underlying meaning. For example "He likes apple" and "Apples are liked by him" should be assigned the same AMR.
 BIBREF0 's approach aimed to produce a summary for a story by extracting a summary subgraph from the story graph and finally generate a summary from this extracted graph. But, because of the unavailability of AMR to text generator at that time their work was limited till extracting the summary graph. This method extracts a single summary graph from the story graph. Extracting a single summary graph assumes that all of the important information from the graph can be extracted from a single subgraph. But, it can be difficult in cases where the information is spread out in the graph. Thus, the method compromises between size of the summary sub-graph and the amount of information it can extract. This can be easily solved if instead of a single sub-graph, we extract multiple subgraphs each focusing on information in a different part of the story.
We propose a two step process for extracting multiple summary graphs. First step is to select few sentences from the story. We use the idea that there are only few sentences that are important from the point of view of summary, i.e. most of the information contained in the summary is present in very few sentences and they can be used to generate the summary. Second step is to extract important information from the selected sentences by extracting a sub-graph from the selected sentences.
Our main contributions in this work are three folds,
The rest of the paper is organized as follows. Section SECREF2 contains introduction to AMR, section SECREF3 and SECREF4 contains the datasets and the algorithm used for summary generation respectively. Section SECREF5 has a detailed step-by-step evaluation of the pipeline and in section SECREF6 we discuss the problems with the current dataset and evaluation metric.
First step is to select few sentences from the story. We use the idea that there are only few sentences that are important from the point of view of summary, i.e. most of the information contained in the summary is present in very few sentences and they can be used to generate the summary. Second step is to extract important information from the selected sentences by extracting a sub-graph from the selected sentences.
Answer: The first step is to select few sentences from the story and finally generate a summary from this extracted graph. The second step is to extract important information from the selected sentences by extracting a sub-graph from the selected sentences.