A Neural Model for Generating Natural Language Summaries of Program Subroutines

Source code summarization -- creating natural language descriptions of source code behavior -- is a rapidly-growing research topic with applications to automatic documentation generation, program comprehension, and software maintenance. Traditional techniques relied on heuristics and templates built manually by human experts. Recently, data-driven approaches based on neural machine translation have largely overtaken template-based systems. But nearly all of these techniques rely almost entirely on programs having good internal documentation; without clear identifier names, the models fail to create good summaries. In this paper, we present a neural model that combines words from code with code structure from an AST. Unlike previous approaches, our model processes each data source as a separate input, which allows the model to learn code structure independent of the text in code. This process helps our approach provide coherent summaries in many cases even when zero internal documentation is provided. We evaluate our technique with a dataset we created from 2.1m Java methods. We find improvement over two baseline techniques from SE literature and one from NLP literature

Do peers see more in a paper than its authors?

Recent years have shown a gradual shift in the content of biomedical publications that is freely accessible, from titles and abstracts to full text. This has enabled new forms of automatic text analysis and has given rise to some interesting questions: How informative is the abstract compared to the full-text? What important information in the full-text is not present in the abstract? What should a good summary contain that is not already in the abstract? Do authors and peers see an article differently? We answer these questions by comparing the information content of the abstract to that in citances-sentences containing citations to that article. We contrast the important points of an article as judged by its authors versus as seen by peers. Focusing on the area of molecular interactions, we perform manual and automatic analysis, and we find that the set of all citances to a target article not only covers most information (entities, functions, experimental methods, and other biological concepts) found in its abstract, but also contains 20% more concepts. We further present a detailed summary of the differences across information types, and we examine the effects other citations and time have on the content of citances

Summarizing Dialogic Arguments from Social Media

Online argumentative dialog is a rich source of information on popular beliefs and opinions that could be useful to companies as well as governmental or public policy agencies. Compact, easy to read, summaries of these dialogues would thus be highly valuable. A priori, it is not even clear what form such a summary should take. Previous work on summarization has primarily focused on summarizing written texts, where the notion of an abstract of the text is well defined. We collect gold standard training data consisting of five human summaries for each of 161 dialogues on the topics of Gay Marriage, Gun Control and Abortion. We present several different computational models aimed at identifying segments of the dialogues whose content should be used for the summary, using linguistic features and Word2vec features with both SVMs and Bidirectional LSTMs. We show that we can identify the most important arguments by using the dialog context with a best F-measure of 0.74 for gun control, 0.71 for gay marriage, and 0.67 for abortion.Comment: Proceedings of the 21th Workshop on the Semantics and Pragmatics of Dialogue (SemDial 2017